Title: Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images
THE 28th IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS
5 - 7 October 2020
Video Link: https://youtu.be/b5tGt6GMN9E
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Semantic Segmentation - Fully Convolutional Networks for Semantic Segmentation岳華 杜
This document discusses several semantic segmentation methods using deep learning, including fully convolutional networks (FCNs), U-Net, and SegNet. FCNs were among the first to use convolutional networks for dense, pixel-wise prediction by converting classification networks to fully convolutional form and combining coarse and fine feature maps. U-Net and SegNet are encoder-decoder architectures that extract high-level semantic features from the input image and then generate pixel-wise predictions, with U-Net copying and cropping features and SegNet using pooling indices for upsampling. These methods demonstrate that convolutional networks can effectively perform semantic segmentation through dense prediction.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
This document summarizes a research paper on DeepFix, a fully convolutional neural network for predicting human eye fixations. DeepFix uses a very deep network with 20 layers and small kernel sizes, inspired by VGG nets. It is a fully convolutional network with convolutional layers replacing fully connected layers to capture global context. The network includes inception layers with parallel kernels of different sizes, and location biased convolutional layers to introduce a center bias. The network is trained end-to-end on datasets of human eye fixations to predict heatmaps of fixation locations. It achieves state-of-the-art results, training in one day on a K40 GPU.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Semantic Segmentation - Fully Convolutional Networks for Semantic Segmentation岳華 杜
This document discusses several semantic segmentation methods using deep learning, including fully convolutional networks (FCNs), U-Net, and SegNet. FCNs were among the first to use convolutional networks for dense, pixel-wise prediction by converting classification networks to fully convolutional form and combining coarse and fine feature maps. U-Net and SegNet are encoder-decoder architectures that extract high-level semantic features from the input image and then generate pixel-wise predictions, with U-Net copying and cropping features and SegNet using pooling indices for upsampling. These methods demonstrate that convolutional networks can effectively perform semantic segmentation through dense prediction.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
This document summarizes a research paper on DeepFix, a fully convolutional neural network for predicting human eye fixations. DeepFix uses a very deep network with 20 layers and small kernel sizes, inspired by VGG nets. It is a fully convolutional network with convolutional layers replacing fully connected layers to capture global context. The network includes inception layers with parallel kernels of different sizes, and location biased convolutional layers to introduce a center bias. The network is trained end-to-end on datasets of human eye fixations to predict heatmaps of fixation locations. It achieves state-of-the-art results, training in one day on a K40 GPU.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
Slide for study session given by Ryosuke Sasaki at Arithmer inc.
It is a summary of recent methods for object pose estimation in robotics using deep learning.
He entered Ph.D course at Univ. of Tokyo in April 2020.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This document discusses semantic image segmentation with deep learning. It begins by defining semantic segmentation as classifying each pixel in an image. Convolutional neural networks (CNNs) can be used for pixel-wise prediction but do not capture spatial context. Conditional random fields (CRFs) can model contextual information but are typically applied as a post-processing step. The document proposes a method called CRF-RNN that integrates CRFs into CNNs by treating mean-field inference as a recurrent neural network. This allows end-to-end training and improves results over applying CRFs as a post-processing step. Examples of semantic segmentation results on various images are shown along with challenges in segmenting certain images.
This document summarizes Pixel Recurrent Neural Networks, proposed models for generative image modeling including PixelRNN and PixelCNN. PixelRNN uses row LSTMs or diagonal bi-LSTMs to capture pixel dependencies while PixelCNN replaces the unbounded dependency with a large bounded receptive field, turning it into a pixel-level classification problem. The models are optimized using techniques like residual connections and masked convolutions. Experiments on MNIST, CIFAR-10, and ImageNet demonstrate state-of-the-art results in log-likelihood and capability of image completion.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
A simple framework for contrastive learning of visual representationsDevansh16
Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
If you'd like to discuss something, text me on LinkedIn, IG, or Twitter. To support me, please use my referral link to Robinhood. It's completely free, and we both get a free stock. Not using it is literally losing out on free money.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Instagram: https://rb.gy/gmvuy9
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
Live conversations at twitch here: https://rb.gy/zlhk9y
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
This document discusses different deep learning approaches for image segmentation. It covers using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). For CNNs, it describes fully convolutional networks, deconvolution networks, U-Net, and other models. For RNNs, it discusses multi-dimensional RNNs, scene labeling with LSTMs, turned and pyramid RNNs, and grid LSTMs. It also reviews Pix2Pix and other GAN-based models for image segmentation tasks.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Conditional Image Generation with PixelCNN Decoderssuga93
The document summarizes research on conditional image generation using PixelCNN decoders. It discusses how PixelCNNs sequentially predict pixel values rather than the whole image at once. Previous work used PixelRNNs, but these were slow to train. The proposed approach uses a Gated PixelCNN that removes blind spots in the receptive field by combining horizontal and vertical feature maps. It also conditions PixelCNN layers on class labels or embeddings to generate conditional images. Experimental results show the Gated PixelCNN outperforms PixelCNN and achieves performance close to PixelRNN on CIFAR-10 and ImageNet, while training faster. It can also generate portraits conditioned on embeddings of people.
The document discusses content-based image retrieval and various techniques used for it. It begins by defining content-based image retrieval as taking a query image and ranking images in a large dataset based on how similar they are to the query. It then covers classic pipelines using SIFT features, using off-the-shelf CNN features, and learning representations specifically for retrieval. Methods discussed include spatial pooling of CNN activations, region pooling like R-MAC, and learning embeddings or features through triplet loss or diffusion-based ranking refinement. The goal is to learn representations from data that effectively capture semantic similarity for retrieval tasks.
Poster - Convolutional Neural Networks for Real-time Road Sign Detection-V3Mr...Guangrui Liu
The document summarizes a convolutional neural network model called YOLO that performs real-time road sign detection in one stage. It divides the input image into a 7x7 grid, with each grid predicting 2 bounding boxes and confidence scores. It is trained on a dataset of 484 stop signs and 284 yield signs over 5000 batches, and tests at over 24 frames per second on videos with an overall accuracy of 92.5%, detecting stop signs at 90% accuracy and yield signs at 95% accuracy.
#6 PyData Warsaw: Deep learning for image segmentationMatthew Opala
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
https://imatge.upc.edu/web/publications/importance-time-visual-attention-models
Bachelor thesis by Marta Cool, advised by Kevin McGuinness (Dublin City University) and Xavier Giro-i-Nieto (Universitat Politecnica de Catalunya).
Predicting visual attention is a very active eld in the computer vision community. Visual attention is a mechanism of the visual system that can select relevant areas within a scene. Models for saliency prediction are intended to automatically predict which regions are likely to be attended by a human observer. Traditionally, ground truth saliency maps are built using only the spatial position of the fixation points, being these xation points the locations where an observer fixates the gaze when viewing a scene. In this work we explore encoding the temporal information as well, and assess it in the application of prediction saliency maps with deep neural networks. It has been observed that the later fixations in a scanpath are usually selected randomly during visualization, specially in those images with few regions of interest. Therefore, computer vision models have dificulties learning to predict them. In this work, we explore a temporal weighting over the saliency maps to better cope with this random behaviour. The newly proposed saliency representation assigns dierent weights depending on the position in the sequence of gaze fixations, giving more importance to early timesteps than later ones. We used this maps to train MLNet, a state of the art for predicting saliency maps. MLNet predictions were evaluated and compared to the results obtained when the model has been trained using traditional saliency maps. Finally, we show how the temporally weighted saliency maps brought some improvement when used to weight the visual features in an image retrieval task.
[DL輪読会]Learning Visible Connectivity Dynamics for Cloth Smoothing (CoRL2021)Deep Learning JP
This paper proposes a method called VCD that applies GNS (a GNN-based dynamics learning architecture) to the task of cloth smoothing. VCD uses a simulator to learn cloth dynamics and successfully transfers zero-shot to the real world. It uses two GNNs - EdgeGNN to model edge connections and DynamicsGNN to model dynamics. EdgeGNN predicts whether edges connect parts of the cloth mesh or are just close collisions. DynamicsGNN predicts point accelerations from partial observations to smooth the cloth. Planning selects actions to maximize cloth spread predicted by DynamicsGNN. Evaluation shows VCD outperforms baselines and can smooth real cloth with only point cloud observations.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Slide for study session given by Dr. Enrico Rinaldi at Arithmer inc.
It is a summary of established methods for parametric modeling of 3D human body "SMPL", which has many possible applications in apparel/health care industry.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
Improved UNet Framework with attention for Semantic Segmentation of Tumor Reg...IRJET Journal
The document proposes an improved UNet framework with attention for semantic segmentation of tumor regions in brain MRI images. The authors develop a variation of the UNet model that incorporates batch normalization after each convolution layer. They train the model in batches and evaluate it using the Intersection over Union metric, which is well-suited for foreground/background segmentation tasks. With their proposed methodology, they achieve an averaged IoU of 84.3% and dice coefficient value of 91.4%, demonstrating the effectiveness of their improved UNet model for segmenting tumor regions in brain MRI images.
Convolutional Neural Network Based Method for Accurate Brain Tumor Detection ...IRJET Journal
This document proposes a convolutional neural network (CNN) based method for accurate brain tumor detection in MRI images to improve robustness. The method aims to enhance detection accuracy and identify tumor boundaries while differentiating tumor regions from healthy tissue. Experimental results using a large annotated MRI image dataset demonstrate the proposed method achieves superior performance compared to existing approaches. The achieved accuracy, efficiency and specificity validate the effectiveness of the CNN-based method for accurate brain tumor detection, with potential to improve clinical decision-making and patient outcomes.
Slide for study session given by Ryosuke Sasaki at Arithmer inc.
It is a summary of recent methods for object pose estimation in robotics using deep learning.
He entered Ph.D course at Univ. of Tokyo in April 2020.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This document discusses semantic image segmentation with deep learning. It begins by defining semantic segmentation as classifying each pixel in an image. Convolutional neural networks (CNNs) can be used for pixel-wise prediction but do not capture spatial context. Conditional random fields (CRFs) can model contextual information but are typically applied as a post-processing step. The document proposes a method called CRF-RNN that integrates CRFs into CNNs by treating mean-field inference as a recurrent neural network. This allows end-to-end training and improves results over applying CRFs as a post-processing step. Examples of semantic segmentation results on various images are shown along with challenges in segmenting certain images.
This document summarizes Pixel Recurrent Neural Networks, proposed models for generative image modeling including PixelRNN and PixelCNN. PixelRNN uses row LSTMs or diagonal bi-LSTMs to capture pixel dependencies while PixelCNN replaces the unbounded dependency with a large bounded receptive field, turning it into a pixel-level classification problem. The models are optimized using techniques like residual connections and masked convolutions. Experiments on MNIST, CIFAR-10, and ImageNet demonstrate state-of-the-art results in log-likelihood and capability of image completion.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
A simple framework for contrastive learning of visual representationsDevansh16
Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
If you'd like to discuss something, text me on LinkedIn, IG, or Twitter. To support me, please use my referral link to Robinhood. It's completely free, and we both get a free stock. Not using it is literally losing out on free money.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Instagram: https://rb.gy/gmvuy9
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
Live conversations at twitch here: https://rb.gy/zlhk9y
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
This document discusses different deep learning approaches for image segmentation. It covers using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). For CNNs, it describes fully convolutional networks, deconvolution networks, U-Net, and other models. For RNNs, it discusses multi-dimensional RNNs, scene labeling with LSTMs, turned and pyramid RNNs, and grid LSTMs. It also reviews Pix2Pix and other GAN-based models for image segmentation tasks.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Conditional Image Generation with PixelCNN Decoderssuga93
The document summarizes research on conditional image generation using PixelCNN decoders. It discusses how PixelCNNs sequentially predict pixel values rather than the whole image at once. Previous work used PixelRNNs, but these were slow to train. The proposed approach uses a Gated PixelCNN that removes blind spots in the receptive field by combining horizontal and vertical feature maps. It also conditions PixelCNN layers on class labels or embeddings to generate conditional images. Experimental results show the Gated PixelCNN outperforms PixelCNN and achieves performance close to PixelRNN on CIFAR-10 and ImageNet, while training faster. It can also generate portraits conditioned on embeddings of people.
The document discusses content-based image retrieval and various techniques used for it. It begins by defining content-based image retrieval as taking a query image and ranking images in a large dataset based on how similar they are to the query. It then covers classic pipelines using SIFT features, using off-the-shelf CNN features, and learning representations specifically for retrieval. Methods discussed include spatial pooling of CNN activations, region pooling like R-MAC, and learning embeddings or features through triplet loss or diffusion-based ranking refinement. The goal is to learn representations from data that effectively capture semantic similarity for retrieval tasks.
Poster - Convolutional Neural Networks for Real-time Road Sign Detection-V3Mr...Guangrui Liu
The document summarizes a convolutional neural network model called YOLO that performs real-time road sign detection in one stage. It divides the input image into a 7x7 grid, with each grid predicting 2 bounding boxes and confidence scores. It is trained on a dataset of 484 stop signs and 284 yield signs over 5000 batches, and tests at over 24 frames per second on videos with an overall accuracy of 92.5%, detecting stop signs at 90% accuracy and yield signs at 95% accuracy.
#6 PyData Warsaw: Deep learning for image segmentationMatthew Opala
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
https://imatge.upc.edu/web/publications/importance-time-visual-attention-models
Bachelor thesis by Marta Cool, advised by Kevin McGuinness (Dublin City University) and Xavier Giro-i-Nieto (Universitat Politecnica de Catalunya).
Predicting visual attention is a very active eld in the computer vision community. Visual attention is a mechanism of the visual system that can select relevant areas within a scene. Models for saliency prediction are intended to automatically predict which regions are likely to be attended by a human observer. Traditionally, ground truth saliency maps are built using only the spatial position of the fixation points, being these xation points the locations where an observer fixates the gaze when viewing a scene. In this work we explore encoding the temporal information as well, and assess it in the application of prediction saliency maps with deep neural networks. It has been observed that the later fixations in a scanpath are usually selected randomly during visualization, specially in those images with few regions of interest. Therefore, computer vision models have dificulties learning to predict them. In this work, we explore a temporal weighting over the saliency maps to better cope with this random behaviour. The newly proposed saliency representation assigns dierent weights depending on the position in the sequence of gaze fixations, giving more importance to early timesteps than later ones. We used this maps to train MLNet, a state of the art for predicting saliency maps. MLNet predictions were evaluated and compared to the results obtained when the model has been trained using traditional saliency maps. Finally, we show how the temporally weighted saliency maps brought some improvement when used to weight the visual features in an image retrieval task.
[DL輪読会]Learning Visible Connectivity Dynamics for Cloth Smoothing (CoRL2021)Deep Learning JP
This paper proposes a method called VCD that applies GNS (a GNN-based dynamics learning architecture) to the task of cloth smoothing. VCD uses a simulator to learn cloth dynamics and successfully transfers zero-shot to the real world. It uses two GNNs - EdgeGNN to model edge connections and DynamicsGNN to model dynamics. EdgeGNN predicts whether edges connect parts of the cloth mesh or are just close collisions. DynamicsGNN predicts point accelerations from partial observations to smooth the cloth. Planning selects actions to maximize cloth spread predicted by DynamicsGNN. Evaluation shows VCD outperforms baselines and can smooth real cloth with only point cloud observations.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Slide for study session given by Dr. Enrico Rinaldi at Arithmer inc.
It is a summary of established methods for parametric modeling of 3D human body "SMPL", which has many possible applications in apparel/health care industry.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
Improved UNet Framework with attention for Semantic Segmentation of Tumor Reg...IRJET Journal
The document proposes an improved UNet framework with attention for semantic segmentation of tumor regions in brain MRI images. The authors develop a variation of the UNet model that incorporates batch normalization after each convolution layer. They train the model in batches and evaluate it using the Intersection over Union metric, which is well-suited for foreground/background segmentation tasks. With their proposed methodology, they achieve an averaged IoU of 84.3% and dice coefficient value of 91.4%, demonstrating the effectiveness of their improved UNet model for segmenting tumor regions in brain MRI images.
Convolutional Neural Network Based Method for Accurate Brain Tumor Detection ...IRJET Journal
This document proposes a convolutional neural network (CNN) based method for accurate brain tumor detection in MRI images to improve robustness. The method aims to enhance detection accuracy and identify tumor boundaries while differentiating tumor regions from healthy tissue. Experimental results using a large annotated MRI image dataset demonstrate the proposed method achieves superior performance compared to existing approaches. The achieved accuracy, efficiency and specificity validate the effectiveness of the CNN-based method for accurate brain tumor detection, with potential to improve clinical decision-making and patient outcomes.
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural ...IRJET Journal
MIScnn is an open-source Python framework that allows researchers to quickly build medical image segmentation pipelines using convolutional neural networks and deep learning models. The framework provides utilities for data input/output, preprocessing, data augmentation, patch-wise analysis, training and evaluating deep learning models. The authors demonstrate the framework's ability to create a segmentation pipeline for a kidney tumor dataset using only a few lines of code. The goal of MIScnn is to provide an intuitive API for rapidly developing medical image segmentation applications.
Enhancing Medical Image Segmentation using Deep Learning: Exploring State-of-...IRJET Journal
This document discusses enhancing medical image segmentation using deep learning models and loss functions that are robust to class imbalanced datasets. Specifically, it explores state-of-the-art models like U-Net, ResUNet, and Attention U-Net combined with loss functions such as focal loss and dice loss that are more effective for datasets where tumor pixels are significantly fewer than other tissue types. The document analyzes the performance of these models and loss functions on two medical imaging datasets: CVC Clinic DB and ISIC 2018 Skin Lesion Dataset, both of which exhibit class imbalance.
Retinal Vessel Segmentation using Infinite Perimeter Active Contour with Hybr...IRJET Journal
This document proposes a retinal vessel segmentation method using an infinite perimeter active contour model with hybrid region information. It first enhances retinal images using three filters: an eigen value based filter, isotropic undecimated wavelet filter, and local phase based filter. It then segments the vessels from the enhanced images using the proposed infinite active contour model. When tested on two public datasets, the local phase based enhancement achieved the best segmentation accuracy compared to the other filters, with a sensitivity of 9.056% and accuracy of 96.52% on the DRIVE dataset. The proposed segmentation method outperforms most existing approaches in terms of segmentation performance.
Microarray spot partitioning by autonomously organising maps through contour ...IJECEIAES
In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization.
1. The document discusses using machine learning techniques like deep learning algorithms and convolutional neural networks to detect diabetic retinopathy in retinal images.
2. It proposes using the MobileNetV2 architecture with SVM classification on the APTOS 2019 dataset to classify retinal images as normal or abnormal.
3. The results obtained after applying SVM classification to the APTOS 2019 dataset using MobileNetV2 showed 87% accuracy and a quadratic weighted kappa score of 0.937, indicating the model can accurately detect diabetic retinopathy.
IRJET - Fusion of CT and MRI for the Detection of Brain Tumor by SWT and Prob...IRJET Journal
This document summarizes a research paper that proposes a method for detecting brain tumors by fusing CT and MRI images using stationary wavelet transform and a probabilistic neural network classifier. The proposed method involves preprocessing the CT and MRI images using median filtering for noise removal. It then applies stationary wavelet transform to the images to extract features before segmenting the tumor region using k-means clustering. Finally, the probabilistic neural network classifier determines if the tumor is benign or malignant based on the fused image features. The paper reviews other existing fusion and classification methods and argues that the proposed stationary wavelet transform and probabilistic neural network approach provides better detection of brain tumors.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
IRJET- Brain Tumor Detection using Deep LearningIRJET Journal
This document discusses using deep learning techniques for brain tumor detection from MRI images. It begins with an abstract that outlines the key steps in the brain tumor detection process: image pre-processing, segmentation, feature extraction, and classification. It then provides more details on each step. Specifically, it proposes using a Convolutional Neural Network (CNN) classifier to overcome limitations of existing techniques. The CNN model would compare trained and test data to classify images and detect tumors. Finally, the document provides background on CNNs, describing their architecture including convolutional, pooling, and fully connected layers, and how they can be used to extract features from medical images for tumor detection.
IRJET - 3D Reconstruction and Modelling of a Brain MRI with TumourIRJET Journal
The document describes a process to 3D print a model of a brain with a tumour using MRI data. The key steps are:
1. Pre-processing the MRI data through filtering and enhancement to highlight the tumour region.
2. Reconstructing the pre-processed images into a 3D model and converting it to an .STL format for 3D printing.
3. 3D printing the model using stereolithography to create a physical replica of the patient's brain specifying the location, size and position of the tumour.
The goal is to create models for pre-surgery planning and simulation to help doctors choose the best surgical procedure.
Anomaly detection using deep learning based model with feature attentionIAESIJAI
Anomaly detection is a difficult problem with numerous industrial applications, such as analyzing the quality of objects using images. Anomaly detection is the process of identifying outliers in a given dataset. Recently, machine learning approaches to computer vision problems have outperformed classical state-of-the-art approaches. Anomaly detection problems can be solved using supervised approaches. However, labelled datasets are hard to obtain. Thus, many researchers have taken an unsupervised approach to solving the problem of anomaly detection. In this study, we use an adversarial auto encoder model as a base model and create a custom model to detect anomalies in images and videos. The model was trained exclusively on normal data. The modified national institute of standards and technology database (MNIST) dataset achieved an area under curve (AUC) score of 0.872 for anomaly detection, while the University of California San Diego (UCSD) anomaly dataset (Video dataset) achieved an AUC score of 0.74 for Ped1 and 0.87 for Ped2. To calculate the anomaly score, the concept of attention weights is combined with the reconstruction loss, and the proposed method outperformed other similar methods designed for the same problem. However, the usefulness of the proposed model was demonstrated through the detection of anomalies, and the model is still being improved for use in real-world situations.
This document presents Jeevn-Net, a new neural network architecture for brain tumor segmentation and overall survival prediction. Jeevn-Net uses a cascaded U-Net structure with two U-Nets and applies auto-encoder regularization. It takes in MRI scans and outputs a segmented tumor image with extracted features. Random forest regression is then used to predict survival based on these features. The network achieves state-of-the-art performance for brain tumor segmentation and survival prediction.
IRJET - Deep Learning based Bone Tumor Detection with Real Time DatasetsIRJET Journal
This document presents a proposed method for detecting bone tumors using deep learning and recurrent neural networks. Specifically, it involves using MRI images as input data and extracting features through segmentation and techniques like HOG. Recurrent neural networks like simple RNNs and LSTMs are then used to both impute any missing data in images and predict bone tumors. This approach is meant to increase accuracy over other methods by handling missing image parts. The proposed system is analyzed to show it can provide accurate bone tumor detection and diagnostic suggestions when evaluating medical examination data.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
Prediction of Cognitive Imperiment using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) model to predict cognitive impairment based on MRI data. It describes collecting MRI reports from various sources to create training and test datasets divided into categories for Alzheimer's dementia, healthy controls, and mild cognitive impairment. The CNN model is trained on this data to differentiate between stages of illness. Results showed the CNN approach achieved accuracy of 81.96% for sensitivity, 71.35% for specificity, and 89.72% for precision, outperforming other state-of-the-art methods by around 5%. The proposed system uses CNN to automatically learn features from raw MRI images without need for manual feature extraction, allowing for a more objective and less biased prediction of cognitive impairment.
DEVELOPMENT OF FAST AND ROBUST IMAGE REGISTRATION METHOD USING DISTANCE MEASU...IRJET Journal
This document discusses the development of a fast and robust method for medical image registration using distance measures to improve medical imaging. It proposes calculating performance metrics based on brain MRI and CT images to create a stiff registration method between CT and MRI images using wavelet fusion. It introduces calculating error rates and time taken based on transforming reference and floating images. The method involves normalizing, transforming and registering images. It analyzes the elapsed time and error rates of the proposed registration technique and discusses how accurate medical imaging through efficient registration is important for analyzing vast amounts of medical image data.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
IRJET- Blood Vessel Segmentation & Analysis in Retinal Images using Image Pro...IRJET Journal
This document discusses a proposed method for segmenting and analyzing blood vessels in retinal images using image processing techniques. It begins with an abstract that outlines segmenting retinal images to extract blood vessels in order to analyze features like thickness and width that can help in diagnosing diseases. It then provides more details on the proposed method which includes steps like image segmentation, vessel enhancement filtering using techniques like median filtering, morphological reconstruction filtering, and using a K-nearest neighbors algorithm for classification. The key steps and techniques are illustrated through a flowchart and example results of applying the method to a retinal image.
Similar to Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images (20)
Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method
Published in: 2019 Medical Technologies Congress (TIPTEKNO)
DOI: 10.1109/TIPTEKNO.2019.8894978
Publisher: IEEE
Conference Location: Izmir, Turkey
Mreps efficient and flexible detection of tandem repeats in DNA
In this paper, we describe mreps, a powerful software tool for a fast identification of tandemly repeated structures in DNA sequences. mreps is able to identify all types of tandem repeats within a single run on a whole genomic sequence. It has a resolution parameter that allows the program to identify 'fuzzy' repeats.
1) The document presents research on using deep neural networks and transfer learning to improve virtual screening for drug discovery.
2) The researchers trained protein family-specific models using the DenseNet architecture on different sized training sets and evaluated using transfer learning and fine-tuning.
3) The results showed that the protein family-specific models outperformed baseline models on standard evaluation metrics, highlighting both the importance of more target-specific models and the need for more data to train such models.
In this project, we use leverage of centrality models for extracting the importance
of network graph in some determined topologies. The aim is to have scrutinizing
and analyzing the centralities in different network topologies. Three type of centrality
that are used in this project are Betweenness, Closeness and eigenvector
one. Moreover, we have show the results of this comparison in the experimental
results. Besides, we have extend the results of our experimental works for real
world problems. The Results of this part are grasped with visualization plots for
some centralities measurements clearly.
This document presents MLProph, a machine learning-based routing protocol for opportunistic networks. It uses decision trees and neural networks to select the next hop for packet forwarding. Simulation results show that MLProph achieves higher delivery probability and lower packet dropping than the PROPHET+ routing protocol. Future work will involve simulating MLProph using real mobility traces and exploring other machine learning classifiers.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
This document discusses two scenarios for waste collection using algorithms. Scenario 1 models waste collection as a traveling salesman problem that is solved using Dijkstra's algorithm. The total cost for collecting waste from 18 bins over 24 hours is calculated as $26,040. Scenario 2 collects waste from 18 bins using Dijkstra's algorithm to find the shortest paths and connect bins to their nearest neighbors, but has a syntax error preventing cost evaluation for different bin numbers. The document suggests prioritizing bins based on waste generation rates to reduce overflow and unnecessary alerts.
There is a smart airport application among the other applications under the SITA company [5] which is produced to provide various information, suggestions to the passengers during the travel by sharing these with the smart phone. In this report, I will extend and scrutiny this application and give my suggestions base on SITA application, I will define the usage and benefit of such smart airport application for airports and passengers.
Udacity Self-Driving Car Engineer Nanodegree Advanced Lane Finding Project. Identifying lanes using edge detection (Sober operator, gradient of magnitude and direction, and HLS color space), camera calibration and unwarping (distortion correction and perspective transform), and polynomial fitting for the lanes.
This document discusses dog breed identification using deep learning models. It provides an overview of traditional and deep learning methods for image classification, including CNN architectures like AlexNet, VGG, GoogleNet, ResNet and DenseNet. The challenge is to identify the breed of dogs in images using pre-trained models. The document analyzes the provided training and test data, which contains over 10,000 images across 120 breeds. It then details the method used, which is to fine-tune popular models like DenseNet, ResNet and GoogleNet. The results show that DenseNet-169 achieved the best validation accuracy of 81.77% and lowest validation loss of 0.6393.
This document summarizes a student's term project on lane identification in autonomous vehicles. The project pipeline involves camera calibration, perspective transformation, color and gradient thresholding to identify lane lines, and lane detection by fitting windows to identified lines. Results showed good performance on straight lanes but difficulty fitting curved lanes. Further work proposed includes providing distance to lane center, improving curved lane detection, and implementing lane identification in video.
This document presents a project on lane finding for autonomous driving. The goals are to develop a solution to detect lane lines on the road to enable autonomous navigation. Related work in this area is discussed, including approaches using motion estimation, end-to-end learning from video datasets, and traditional computer vision techniques. Risks that could impact the accuracy of lane detection are also summarized, such as curved roads, weather conditions, shadows, and irregular road markings. The document outlines the work breakdown structure and Gantt chart for the project and provides references for related research.
This document summarizes a survey paper on smart charging for electric vehicles from an algorithmic perspective. It discusses smart grid-oriented EV charging approaches like load flattening, frequency regulation, and voltage regulation. It also discusses aggregator-oriented and customer-oriented EV charging approaches and the uncertainties involved. Future work opportunities are identified in areas like battery modeling, routing, and communication requirements to further the smart interaction between electric vehicles and the smart grid.
The document discusses smart airport applications and their benefits. It describes how smart airport applications can provide real-time information to passengers, such as baggage tracking and flight status updates. The applications also offer personalized suggestions to help passengers navigate the airport efficiently, such as recommendations for parking or places to wait with less crowds. Digital boarding passes are highlighted as another smart feature that can streamline the travel process through self-service checkpoints. In conclusion, smart airports are expected to improve the travel experience by optimizing and customizing services for passengers.
this presentation file lectured in international conference in new research of Electrical and engineering and computer science.
Abstract
This paper presents a novel and uniform algorithm for edge detection based on SVM (support vector machine) with Three-dimensional Gaussian radial basis function with kernel. Because of disadvantages in traditional edge detection such as inaccurate edge location, rough edge and careless on detect soft edge. The experimental results indicate how the SVM can detect edge in efficient way. The performance of the proposed algorithm is compared with existing methods, including Sobel and canny detectors. The results shows that this method is better than classical algorithm such as canny and Sobel detector.
مسیریابی سیستم های خودمختارکه در این اسلاید در باره ی شبکه های مسیریابی بین سیستم های خود مختار و نحوه ی ایجاد کانکشن بین شبکه ها مورد بررسی قرار می گیرد.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf
Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images
1. Deep Learning based Segmentation Pipeline
for Label-Free Phase-Contrast Microscopy Images
Aydin Ayanzadeh, ¨Ozden Yal¸cın ¨Ozuysal, Devrim Pesen Okvur, Sevgi
¨Onal, Beh¸cet U˘gur T¨oreyin, Devrim ¨Unay
Istanbul Technical University, Izmir Institute of Technology,
Izmir Demokrasi University
September 23, 2020
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 1 / 20
3. Problem Motivation
Segmentation of medical images especially on microscopy images is
challenging.
segmentation make ease the process of cell behaviours.
Reduce the workload and provide oppurtunity for batch analysis of the
cells.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 3 / 20
4. Literature Review
U-Net[1] and its variants
Maskrcnn[2]
Figure: Representation of U-Net Architecture[1].
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 4 / 20
5. Dataset
MDA MB-213
Invasive breast cancer cells that which has mesenchymal morphology.
Dataset collected and annotated with the help of experts.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 5 / 20
6. Dataset Preparation
Figure: Workflow of Dataset Preparation.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 6 / 20
7. Model
Figure: Representation of ResNet-18 Model[3].
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 7 / 20
8. Model
ResNet18-FPN
Applying Pretrained ResNet18 in the encoder of FPN.
Representation of ResNet-18-FPN Model.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 8 / 20
9. Model
ResNet18-UNet
ResNet18 is replaced in the encoder of UNet.
Residual block is replaced in the decoder section.
Figure: Representation of ResNet-18-U-Net Model.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 9 / 20
10. Training Methodology
The encoder has pre-train of ImageNet.
Test Time Augmentation(TTA)
Loss functions: Binary Cross Entropy loss + Dice Loss
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 10 / 20
11. Evaluation Metrics
Evaluation Metrics
Precision, Recall, Dice Coefficient, Jaccard Index (IoU)
Precision =
ntp
nfp + ntp
(1)
Recall =
ntp
nfn + ntp
(2)
Jaccard(X, Y ) =
|X Y |
|X Y |
=
|X Y |
|X| + |Y | − |X Y |
(3)
Dice(X, Y ) =
2|X · Y |
|X| + |Y |
(4)
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 11 / 20
14. Conclusion
Applying alternative encoder(ResNet18) on U-Net and FPN.
The model with alternative encoder and decoder is more robust to
outlier and boundary regions.
Applying pre-trained of ImageNet is successful in convergence to
better results.
Reduce the disparity in the encoder feature and the features that
propagate in the decoder of the U-Net architecture.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 14 / 20
15. Future Work
Deploy methods for instance segmentation tasks and make it robust
for tracking purpose.
Applying the alternative encoder to gain better results.
Evaluate the performance methods on different modality in medical
image datasets.
Extraction of the time-series dataset by the construction of lineage
relationships among the cells.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 15 / 20
17. References I
Olaf Ronneberger, Philipp Fischer, and Thomas Brox.
U-net: Convolutional networks for biomedical image segmentation.
In Nassir Navab, Joachim Hornegger, William M. Wells, and
Alejandro F. Frangi, editors, Medical Image Computing and
Computer-Assisted Intervention – MICCAI 2015, pages 234–241,
Cham, 2015. Springer International Publishing.
Hsieh-Fu Tsai, Joanna Gajda, Tyler F.W. Sloan, Andrei Rares, and
Amy Q. Shen.
Usiigaci: Instance-aware cell tracking in stain-free phase contrast
microscopy enabled by machine learning.
SoftwareX, 9:230 – 237, 2019.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
Deep residual learning for image recognition.
In Proceedings of the IEEE conference on computer vision and pattern
recognition, pages 770–778, 2016.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 17 / 20
18. References II
Joe Chalfoun, M Majurski, A Peskin, Catherine Breen, Peter Bajcsy,
and M Brady.
Empirical gradient threshold technique for automated segmentation
across image modalities and cell lines.
Journal of microscopy, 260(1):86–99, 2015.
Nicolas Jaccard, Lewis D Griffin, Ana Keser, Rhys J Macown,
Alexandre Super, Farlan S Veraitch, and Nicolas Szita.
Automated method for the rapid and precise estimation of adherent
cell culture characteristics from phase contrast microscopy images.
Biotechnology and bioengineering, 111(3):504–517, 2014.
Abhishek Chaurasia and Eugenio Culurciello.
Linknet: Exploiting encoder representations for efficient semantic
segmentation.
In 2017 IEEE Visual Communications and Image Processing (VCIP),
pages 1–4. IEEE, 2017.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 18 / 20
19. References III
Vladimir Iglovikov and Alexey Shvets.
Ternausnet: U-net with VGG11 encoder pre-trained on imagenet for
image segmentation.
CoRR, abs/1801.05746, 2018.
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 19 / 20
20. Deep Learning based Segmentation Pipeline
for Label-Free Phase-Contrast Microscopy Images
Aydin Ayanzadeh, ¨Ozden Yal¸cın ¨Ozuysal, Devrim Pesen Okvur, Sevgi
¨Onal, Beh¸cet U˘gur T¨oreyin, Devrim ¨Unay
Istanbul Technical University, Izmir Institute of Technology,
Izmir Demokrasi University
September 23, 2020
Aydin Ayanzadeh (SpC4ing Group./˙IT¨U) Cell Segmentation of PCM. September 23, 2020 20 / 20