Image Segmentation: Approaches and Challenges
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This slides go over the problem of deep semantic segmentation. It covers the different approaches taken, from hourglass autoencoder to pyramid networks. Slides by Thomas Delteil
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Automatic gender and age classification has become quite relevant in the rise of social media platforms. However, the existing methods have not been completely successful in achieving this. Through this project, an attempt has been made to determine the gender and age based on a frame of the person. This is done by using deep learning, OpenCV which is capable of processing the real-time frames. This frame is given as input and the predicted gender and age are given as output. It is difficult to predict the exact age of a person using one frame due the facial expressions, lighting, makeup and so on so for this purpose various age ranges are taken, and the predicted age falls in one of them. The Adience dataset is used as it is a benchmark for face photos and includes various real-world imaging conditions like noise, lighting etc.
Enhancing Social Network Security through Smart Credentials
This document proposes enhancing social network security through smart credentials. It discusses how current social networks have weak authentication that allows identity cloning attacks. The document then presents using discrete wavelet transform for data hiding and watermarking uploaded images to help prevent clone attacks. It provides block diagrams of the proposed transmitter and receiver systems. When a user uploads an image, it would be watermarked with their credentials. This allows detecting if another user tries to use the same image to create a fake profile. Overall, the proposed system aims to provide more secure authentication and prevent clone attacks on social networks.
IRJET- Python Libraries and Packages for Deep Learning-A Survey
This document summarizes a survey of Python libraries and packages that are commonly used for deep learning. It discusses several popular deep learning frameworks like TensorFlow, Keras, Caffe, PyTorch, and Theano that can be used with Python. It also summarizes several research papers that utilized these Python deep learning libraries and packages to implement applications like image classification on embedded devices, mobile edge caching using deep learning, and high performance text recognition. The document highlights the benefits of using Python for deep learning due to its extensive library support, simplicity, reliability, and ease of developing applications.
Challenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Slides from the webinar on Challenges of Deep Learning in Computer Vision presented by Tessellate Imaging and powered by E2E Networks.
The webinar discusses the growth and applications of Computer Vision in modern-day real life. Challenges with implementing and developing Deep Learning and Computer Vision projects for both enterprises and developers.
We introduce MonkAI (https://monkai.org) an Open Sourced Deep Learning wrapper library for Computer Vision development and talk about features tackling some of the challenges in Deep Learning.
Ijeee 16-19-digital media hidden data extracting
Abstract— Magnetic Resonance Imaging (MRI) brain tumor image classification is a difficult task due to the variance and complexity of tumors. This paper presents an efficient techniques for the classification of the magnetic resonance brain images. In this work we are taking MR images as input; MRI which is directed into internal cavity of brain and gives the complete image of the brain. The proposed technique consists of two stages.In the first stage, discrete wavelet transform is used for dimensionality reduction and feature extraction.In the second stage, classification is performed using the probabilistic neural network. The classifier have been used to classify real MR images as benign (non-cancerous) and Malignant (cancerous). Probabilistic neural network (PNN) with image and data processing technique is employed to implement an automated brain tumor classification. The use of artificial intelligent technique has shown great potential in this field.
Index Terms— Brain tumors, Feature extraction,Classification, MRI, Probabilistic neural network, Dimensionality reduction, Discrete wavelet transform.
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Recent Advances in Natural Language Processing
The document provides an overview of recent advances in natural language processing (NLP), including traditional methods like bag-of-words models and word2vec, as well as more recent contextualized word embedding techniques like ELMo and BERT. It discusses applications of NLP like text classification, language modeling, machine translation and question answering, and how different models like recurrent neural networks, convolutional neural networks, and transformer models are used.
Fine-tuning BERT for Question Answering
This deck covers the problem of fine-tuning a pre-trained BERT model for the task of Question Answering. Check out the GluonNLP model zoo here for models and tutorials: http://gluon-nlp.mxnet.io/model_zoo/bert/index.html
Slides: Thomas Delteil
Introduction to GluonNLP
GluonNLP is a deep learning toolkit for Natural Language Processing. These slides covers the motivation behind the creation of the toolkit and what is available in it. Go try it at https://gluon-nlp.mxnet.io!
Introduction to object tracking with Deep Learning
The document discusses object tracking using deep learning. It defines object tracking as locating an object across consecutive video frames. It notes applications in security, road safety, and entertainment. Object tracking differs from object detection in that the object class is unknown during training and tracking considers objects across time rather than individual frames. Challenges include objects leaving the screen or changing pose. The document discusses metrics for evaluating trackers, including accuracy and robustness, and surveys popular modern trackers.
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Introduction to the deep learning Computer Vision toolkit GluonCV and the motivations behind its creation. Go try it!
https://gluon-cv.mxnet.io
Introduction to Computer Vision
This presentation introduces the topic of computer vision, especially through the lense of Deep Learning.
Go build! https://gluon-cv.mxnet.io
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Deep Learning approaches tu face detection and recognition, state-of-art results and discussions of current limitations.
Generative Adversarial Networks (GANs) using Apache MXNet
The document provides an overview of generative adversarial networks (GANs) using Apache MXNet. It introduces GANs and deep learning concepts. It then demonstrates how to implement GANs using MXNet with examples like DCGAN. Finally, it discusses other GAN models and provides resources for using MXNet on AWS.
Deep Learning With Apache MXNet On Video by Ben Taylor @ ziff.ai
This talk will go over using Apache MXNet on video streams such as security footage from Ring, or live XBOX video data to perform inference and indexing. This can be used to classify video events, detect anomalies in normal behavior, and search. This talk will focus on using FFMPEG for feeding Apache MXNet models for fast inference throughput and performance. This talk will also discuss the difference between frame level inference, and frame buffer inference (comprehending a temporal video event).
Links to videos on the slides:
IntelAct: Winner, Visual Doom AI Competition, Full Deathmatch: https://www.youtube.com/watch?v=947bSUtuSQ0
GPU assisted call of duty processing, prep for AI auto-play: https://www.youtube.com/watch?v=gTXOYzSC_ZE
Presented at https://www.meetup.com/deep-learning-with-mxnet/events/258901722/
Using Java to deploy Deep Learning models with MXNet
The document discusses deep learning and the Apache MXNet framework. It provides an introduction to deep learning concepts like neural networks and machine learning. It then describes MXNet as an open source deep learning framework that supports multiple languages including Java. It outlines how to get started with MXNet's Java API and discusses some technical challenges around Java memory management when using deep learning models.
AI powered emotion recognition: From Inception to Production - Global AI Conf...
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MXNet Paris Workshop - Intro To MXNet
MXNet is a flexible and efficient deep learning framework that is programmable in multiple languages and scalable across multiple GPUs and machines. It originated from the DMLC community and is now an Apache incubating project. MXNet provides low-level NDArray and Symbol APIs as well as high-level Gluon APIs and has additional toolkits like GluonCV for computer vision tasks. MXNet supports distributed training across multiple machines using parameter servers and can serve trained models for low-latency inference using the MXNet Model Server.
Apache MXNet ODSC West 2018
This document provides an overview of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It discusses how RNNs can be used for sequence modeling tasks like sentiment analysis, machine translation, and speech recognition by incorporating context or memory from previous steps. LSTMs are presented as an improvement over basic RNNs that can learn long-term dependencies in sequences using forget gates, input gates, and output gates to control the flow of information through the network.
DeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
What is Deep Learning
Rise of Deep Learning
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Apache MXNet EcoSystem - ACNA2018
The document discusses Apache MXNet, an open-source deep learning framework. It provides an overview of MXNet's history and key features, including support for multiple programming languages, an ecosystem of tools like GluonCV and GluonNLP, and model serving capabilities. It also describes MXNet's use of ONNX for model interchange, integration with Keras, and performance optimization using technologies like CUDA, MKL, and TVM. The document highlights MXNet's large community and adoption by customers.
ONNX and Edge Deployments
In this talk ONNX (Open Neural Network eXchange) is introduced, and the ONNX Model Zoo is used as the base for fine-tuning with AWS SageMaker and Apache MXNet's Gluon API. With a fine-tuned model trained on Caltech101, AWS GreenGrass is discussed for edge deployments and the TVM Stack is suggested as a method for optimising the inference of models on edge devices.
Presented by: Thom Lane at Linaro Connect Vancouver 2018 on 19th September 2018.
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Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.,. Inference on large scale datasets comes with many challenges prevalent in distributed data processing. This presentation will show how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating).
Multivariate Time Series
This presentation describes two major papers in multi-variate time-series using deep neural networks. The first paper, DeepAR was developed at Amazon to deal with forecasting of millions of items where the same model can be applied to millions of products. DeepAR is implemented as a built-in algorithm of Amazon SageMaker. Code example is provided.
The second paper, Long- and Short-Term Temporal Patterns with Deep Neural Networks is developed at CMU and introduces a novel way to detect both short term and long term seasonality in data through introduction of skip-rnn.
A Gluon implementation of the paper is provided in the presentation.
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Inference on edge has an ever increasing performance for companies and thus it is crucial to be able to make models smaller. Compressing models can be loss-less or can result in loss of accuracy. This presentation provides a survey of compression techniques for deep learning models. It then describes different architectures of AWS IoT/Green Grass to combine on-device inference and GPU inference in a hub model. Additionally the presentation introduces MXNet, which has small footprint and efficient both for inference and training in distributed settings.
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Netflix competition triggered a flurry of research for recommendation engines. This presentation provides a survey of techniques and models for creating a recommender system. The presentation covers Matrix Factorisation, Factorisation Machines, Distributed Factorisation Machines, and DSSM networks as well provide code examples for developing a Matrix Factorisation in Gluon. At the end the presentation provides tips and tricks for large-scale, realtime recommender engines.
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Recently uploaded
Randomised Optimisation Algorithms in DAPHNE
Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/
Cytokines and their role in immune regulation.pptx
This presentation covers the content and information on "Cytokines " and their role in immune regulation .
Immersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
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原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Thornton ESPP slides UK WW Network 4_6_24.pdf
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Sharlene Leurig - Enabling Onsite Water Use with Net Zero WaterTexas Alliance of Groundwater Districts
Presented at June 6-7 Texas Alliance of Groundwater Districts Business Meeting8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/Equivariant neural networks and representation theory
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Compexometric titration/Chelatorphy titration/chelating titration
Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
Potentiometry
Miscellaneous methods.
Complex titration with EDTA.
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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Cytokines and their role in immune regulation.pptx
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Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
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Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
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Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Equivariant neural networks and representation theory
Equivariant neural networks and representation theory
Basics of crystallography, crystal systems, classes and different forms
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Compexometric titration/Chelatorphy titration/chelating titration
Compexometric titration/Chelatorphy titration/chelating titration
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless Reproducibility
Image Segmentation: Approaches and Challenges
- 1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Semantic Segmentation
- 2. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Problem statement: Pixel-level classification task
- 3. Applications: Brain tissue segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp Fischer, Thomas Brox, 2015
- 4. source: https://github.com/reachsumit/deep-unet-for-satellite-image-segmentation Applications: Satellite image land use
- 5. Applications: Self-driving cars source: https://www.youtube.com/watch?v=ATlcEDSPWXY
- 6. How does it work? Source: Fully Convolutional Networks for Semantic Segmentation, Long et al. 2015 Deep Neural Network
- 7. Input RGB or Grayscale Images Unsigned integer [0,255]
- 8. N classes Output: predict one “heat-map” per class Softmax across class axis
- 9. How does it work? Trained to minimize the softmax cross entropy loss for each pixel i,j predictions among the N different classes: 𝑙𝑜𝑠𝑠 = − 𝑖,𝑗 𝐻,𝑊 𝑐 𝑁 𝑦𝑖,𝑗,𝑐 ∗ log(𝑝𝑖,𝑗,𝑐) 𝑙𝑜𝑠𝑠 = − 𝑖,𝑗 𝐻,𝑊 log(𝑝𝑖,𝑗,𝑐=𝑦 𝑖,𝑗 )
- 10. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Main challenge: capturing multi-scale context cow?
- 11. Source: Deep LabV3 Rethinking Atrous Convolution for Semantic Image Segmentation, Chen et al. 2017 Strategies for capturing multi-scale context
- 12. Architectures: HourGlass Architecture of the full network. The convolution network is based on the VGG16 architecture. The deconvolution network uses unpooling and deconvolution layers. Source: H. Noh et al. (2015)
- 13. Architectures: U-Net U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp Fischer, Thomas Brox, 2015
- 14. Architectures: DeepLab V3 Source: Rethinking Atrous Convolution for Semantic Image Segmentation Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam, 2017
- 15. Architectures: DeepLab V3+ Source: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam, 2018
- 16. Architectures: and more See this medium blog post: Review of deep learning algorithm for semantic segmentation Fully Convolutional Network ParseNet Feature Pyramid Network Pyramid Scene Parsing network (PSPNet) Path Aggregation Network (PANet) Context Encoding Network (EncNet)
- 17. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark
- 18. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Conclusion The key challenge in semantic segmentation is to efficiently mix local and global context for pixel-wise predictions
- 19. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Thank you! Go Build! https://gluon-cv.mxnet.io/build/examples_segmentation/index.html
Editor's Notes
- First call deck for a high level introduction to Apache MXNet.
- Pixel house landscape by 8bitnoob