[5분 논문요약] Structured Knowledge Distillation for Semantic Segmentation
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[Conference paper summary] Title: Structured Knowledge Distillation for Semantic Segmentation (CVPR 2019 accepted) Author: Liu et al. Video: https://youtu.be/n3BxiTmewMM
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https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
SelfCon_AAAI.pdf
1) The paper proposes Self-Contrastive Learning, which uses a single network to generate multiple outputs from different levels that are then used for self-contrastive learning without data augmentation.
2) This allows implementing a multi-view framework with only a single sample, using the sub-network to provide an alternative feature space view.
3) Experiments show Self-Contrastive Learning outperforms Supervised Contrastive Learning on image classification tasks while being more computationally efficient due to the single-view approach.
Practical tips for handling noisy data and annotaiton
The document summarizes a KaggleDays workshop on techniques for handling noisy data and annotation. It includes an agenda covering an introduction, experiment setup, and techniques for learning with noisy datasets. The techniques discussed are mixup, using large batch sizes, and distillation. For mixup, virtual training samples are constructed by linearly interpolating real samples and labels. Large batch sizes help because noise from random labels cancels out within a batch. Distillation trains a student network using predictions from a pre-trained teacher network to ease training. Code links and examples of applying the techniques in competitions are also provided.
Large scale landuse classification of satellite imagery
Identifying LAnd use from Satellite images with Deep Learning from both RGB and Short-Wave Infra Red bands
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Deep learning in Computer Vision
The document discusses deep learning in computer vision. It provides an overview of research areas in computer vision including 3D reconstruction, shape analysis, and optical flow. It then discusses how deep learning approaches can learn representations from raw data through methods like convolutional neural networks and restricted Boltzmann machines. Deep learning has achieved state-of-the-art results in applications such as handwritten digit recognition, ImageNet classification, learning optical flow, and generating image captions. Convolutional neural networks have been particularly successful due to properties of shared local weights and pooling layers.
(SURVEY) Active Learning
1. The document discusses various active learning methods including uncertainty-based methods that select samples that are hard to learn, and representation-based methods that select a diverse set of samples.
2. Specific methods covered include using dropout, ensembles, and entropy to estimate uncertainty, as well as density-based and variational adversarial approaches.
3. Key papers summarized are Deep Bayesian Active Learning with Image Data (ICML'17), Dropout as a Bayesian Approximation (ICML'16), Cost-Effective Active Learning for Deep Image Classification (2017), and Variational Adversarial Active Learning (ICCV'19).
Deep Learning For Computer Vision- Day 3 Study Jams GDSC Unsri.pptx
Materi "Deep Learning for Computer Vision" mencakup konsep dasar deep learning dengan fokus pada transfer learning menggunakan TensorFlow, evaluasi model, dan proses deployment. Pada intinya, transfer learning memanfaatkan pengetahuan yang telah dimiliki oleh model yang telah dilatih sebelumnya untuk meningkatkan kinerja model pada tugas tertentu. Evaluasi model melibatkan penilaian kualitas dan keandalan model yang telah dibangun, sementara deployment mencakup implementasi model ke lingkungan produksi untuk penggunaan praktis. Materi ini memberikan pemahaman holistik tentang penerapan deep learning dalam konteks computer vision, melibatkan tahapan esensial dari pengembangan hingga implementasi model pada aplikasi dunia nyata.
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Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
Semantic Segmentation - Fully Convolutional Networks for Semantic Segmentation
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CNNs: from the Basics to Recent Advances
The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
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Slides by Thomas Delteil
深度學習在AOI的應用
This document discusses using fully convolutional neural networks for defect inspection. It begins with an agenda that outlines image segmentation using FCNs and defect inspection. It then provides details on data preparation including labeling guidelines, data augmentation, and model setup using techniques like deconvolution layers and the U-Net architecture. Metrics for evaluating the model like Dice score and IoU are also covered. The document concludes with best practices for successful deep learning projects focusing on aspects like having a large reusable dataset, feasibility of the problem, potential payoff, and fault tolerance.
IRJET - Factors Affecting Deployment of Deep Learning based Face Recognition ...
This document discusses factors affecting the deployment of deep learning models for face recognition on smartphones. It examines training data requirements, suitable neural network architectures, and effective loss functions. Larger datasets with more subjects and images are preferred for training models that generalize well. Residual networks like ResNet have achieved good accuracy while being efficient for face recognition. Loss functions like center loss and triplet loss help learn discriminative features by reducing intra-class and increasing inter-class variations.
Learning global pooling operators in deep neural networks for image retrieval...
Learning global pooling operators in deep neural networks for image retrieval...Erlangen Artificial Intelligence & Machine Learning Meetup
Like other fields of computer vision, image retrieval has been
revolutionized by deep learning in recent years. Convolutional neural networks are now the tool of choice for computing feature representations of images. Many successful architectures employ global pooling layers to aggregate feature maps to a compact image representation. Using the neural network training procedure based on backpropagation and gradient descent methods, we can learn the global pooling operation from the training data.
We review existing approaches to learned pooling and propose two new layers: A learnable, extended variant of LSE pooling and the generalized max pooling layer based on an aggregation function from classical computer vision.
Our experiments show that learned global pooling can improve performance of image retrieval networks compared to the average pooling baseline for both tasks. For writer identification, our generalized max pooling layer outperforms all other tested pooling layers. Our learnable LSE pooling performs better than global average pooling and yields the best rank-1 score in our experiments on the Market-1501 dataset.Deep learning
This document provides an overview of machine learning and deep learning concepts. It begins with an introduction to machine learning basics, including supervised and unsupervised learning. It then discusses deep learning, why it is useful, and its main components like activation functions, optimizers, and regularization methods. The document explains deep neural network architecture including convolutional neural networks. It provides examples of convolutional and max pooling layers and how they help reduce parameters in neural networks.
IRJET- Semantic Segmentation using Deep Learning
The document discusses semantic image segmentation using deep learning techniques. It summarizes several state-of-the-art semantic segmentation models like U-Net, Dilated U-Net, PSPNet, Fully Convolutional DenseNets, Global Convolutional Network (GCN), DeepLabV3, and proposes an optimized FRRN model. It implements these models on the CamVid dataset and evaluates their performance using the intersection-over-union score, finding that the optimized FRRN model achieves a score of 0.87.
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More from Sang Jun Lee
Lecture 7: Recurrent Neural Networks
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", 2017-11-10
- Contents: introduction to reccurent neural networks, LSTM, variants of RNN, implementation of RNN, case studies
- Video: https://youtu.be/pgqiEPb4pV8
Lecture 6: Convolutional Neural Networks
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", 2017-10-12
- Contents: components of convolutional neural network (convolution layer & pooling layer), CNN architectures, implementation of a CNN
- Video: https://youtu.be/u07CbFuUpbk
Lecture 5: Neural Networks II
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", Week 5
- Contents: Restricted Boltzmann Machine (RBM), various activation functions, data preprocessing, regularization methods, training of a neural network
- Video: https://youtu.be/v4rGPl-8wdo
Lecture 4: Neural Networks I
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", Week 4
- Contents: perceptron, multilayer perceptron, back propagation, vanishing gradient
- Video: https://youtu.be/3iqOoT6mNhk
Lecture 3: Unsupervised Learning
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", Lecture 3
- Contents: unsupervised learning, k-means algorithm, spectral clustering, anomaly detection
Lecture 2: Supervised Learning
- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", Lecture 2
- Contents: categorization of machine learning, supervised learning (linear regression and logistic regression), multiclass classification
Lecture 1: Introduction to Python and TensorFlow
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[5분 논문요약] Structured Knowledge Distillation for Semantic Segmentation
- 1. Structured Knowledge Distillation for Semantic Segmentation 2019. 4. 10 Sang Jun Lee https://arxiv.org/abs/1903.04197 (CVPR 2019)
- 2. Knowledge Distillation Teacher network (deep) 𝜎 𝜎 𝑧 𝑒 Σ 𝑒 Hard label Loss 0 1 0 0 Student network 𝜎 Loss Soft-label Distillation 0.01 0.83 0.15 0.01
- 3. Knowledge Distillation Model compression (network minimization) Network ensemble Self-distillation
- 4. Structured Knowledge Distillation for Semantic Segmentation Pixel-wise distillation teacher network의 soft-max 출력에서 개별 픽셀에 해당하는 class-probability를 이용 Pair-wise distillation 어떤 feature map에서 paired feature vector들의 similarity를 distillation Distillation of holistic knowledge 영상 전체의 정보를 이용하기 위한 teacher network 출력과 student network 출력 사이의 adversarial learning Structured knowledge
- 6. Method Wasserstein distance : evaluate the difference between real and fake distribution Training Discriminator (maximize) Student network (minimize) Total loss
- 7. Experiment Teacher PSPNet Students ESPNet, ESPNet-C, MobileNetV2Plus, ResNet18 https://arxiv.org/abs/1903.04197