深度学习领域相关资料

本文转载自https://github.com/kjw0612/awesome-deep-vision/blob/master/README.md

Awesome Deep Vision

A curated list of deep learning resources for computer vision, inspired by awesome-php and awesome-computer-vision.

Maintainers - Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim

We are looking for a maintainer! Let me know ([e-mail]) if interested.

Contributing

Please feel free to pull requests to add papers.

Sharing

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Table of Contents

• Papers
  
  • ImageNet Classification
  • Object Detection
  • Object Tracking
  • Low-Level Vision
  • Super-Resolution
  • Other Applications
  • Edge Detection
  • Semantic Segmentation
  • Visual Attention and Saliency
  • Object Recognition
  • Understanding CNN
  • Image and Language
  • Image Captioning
  • Video Captioning
  • Question Answering
  • Other Topics
• Courses
• Books
• Videos
• Software
  
  • Framework
  • Applications
• Tutorials
• Blogs

Papers

ImageNet Classification

(from Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.)
* Microsoft (Deep Residual Learning) [Paper][Slide]
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385.
* Microsoft (PReLu/Weight Initialization) [Paper]
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852.
* Batch Normalization [Paper]
* Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167.
* GoogLeNet [Paper]
* Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR, 2015.
* VGG-Net [Web] [Paper]
* Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Visual Recognition, ICLR, 2015.
* AlexNet [Paper]
* Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.

Object Detection

(from Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.)

• OverFeat, NYU [Paper]
  
  • OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, ICLR, 2014.
• R-CNN, UC Berkeley [Paper-CVPR14] [Paper-arXiv14]
  
  • Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.
• SPP, Microsoft Research [Paper]
  
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
• Fast R-CNN, Microsoft Research [[Paper]] (http://arxiv.org/pdf/1504.08083)
  
  • Ross Girshick, Fast R-CNN, arXiv:1504.08083.
• Faster R-CNN, Microsoft Research [[Paper]] (http://arxiv.org/pdf/1506.01497)
  
  • Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.
• R-CNN minus R, Oxford [[Paper]] (http://arxiv.org/pdf/1506.06981)
  
  • Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.
• End-to-end people detection in crowded scenes [[Paper]] (http://arxiv.org/abs/1506.04878)
  
  • Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.
• You Only Look Once: Unified, Real-Time Object Detection [[Paper]] (http://arxiv.org/abs/1506.02640)
  
  • Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640
• Inside-Outside Net [Paper]
  
  • Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick, Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
• Deep Residual Network (Current State-of-the-Art) [Paper]
  
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition
• Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [[Paper]] (http://arxiv.org/pdf/1503.00949.pdf)

Video Classification

• Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, “Delving Deeper into Convolutional Networks for Learning Video Representations”, ICLR 2016. [Paper]
• Michael Mathieu, camille couprie, Yann Lecun, “Deep Multi Scale Video Prediction Beyond Mean Square Error”, ICLR 2016. [Paper]

Object Tracking

• Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796. [Paper]
• Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014. [Paper]
• N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013. [Paper]
• Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [GitHub]
• Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 [GitHub] [Paper]
• Hyeonseob Nam and Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, [Paper] [Code] [Project Page]

Low-Level Vision

Super-Resolution

• Super-Resolution (SRCNN) [Web] [Paper-ECCV14] [Paper-arXiv15]
  
  • Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.
  • Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, arXiv:1501.00092.
• Very Deep Super-Resolution
  
  • Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015. [Paper]
• Deeply-Recursive Convolutional Network
  
  • Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. [Paper]
• Casade-Sparse-Coding-Network
  
  • Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015. [Paper] [Code]
• Perceptual Losses for Super-Resolution
  
  • Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016. [Paper] [Supplementary]
• Others
  
  • Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014. [Paper ICONIP-2014]

Other Applications

• Optical Flow (FlowNet) [Paper]
  
  • Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks, arXiv:1504.06852.
• Compression Artifacts Reduction [Paper-arXiv15]
  
  • Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.
• Blur Removal
  
  • Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444 [Paper]
  • Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015 [Paper]
• Image Deconvolution [Web] [Paper]
  
  • Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
• Deep Edge-Aware Filter [Paper]
  
  • Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.
• Computing the Stereo Matching Cost with a Convolutional Neural Network [Paper]
  
  • Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.

Edge Detection

(from Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.)

• Holistically-Nested Edge Detection [Paper]
  
  • Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.
• DeepEdge [Paper]
  
  • Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.
• DeepContour [Paper]
  
  • Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015.

Semantic Segmentation

(from Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640.)
* PASCAL VOC2012 Challenge Leaderboard (02 Dec. 2015)

(from PASCAL VOC2012 leaderboards)
* Adelaide
* Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. [Paper] (1st ranked in VOC2012)
* Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. [Paper] (4th ranked in VOC2012)
* Deep Parsing Network (DPN)
* Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 [Paper] (2nd ranked in VOC 2012)
* CentraleSuperBoundaries, INRIA [Paper]
* Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)
* BoxSup [Paper]
* Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th ranked in VOC2012)
* POSTECH
* Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. [Paper] (7th ranked in VOC2012)
* Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924. [Paper]
* Conditional Random Fields as Recurrent Neural Networks [Paper]
* Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. (8th ranked in VOC2012)
* DeepLab
* Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. [Paper] (9th ranked in VOC2012)
* Zoom-out [Paper]
* Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015
* Joint Calibration [Paper]
* Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.
* Fully Convolutional Networks for Semantic Segmentation [Paper-CVPR15] [Paper-arXiv15]
* Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
* Hypercolumn [Paper]
* Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015.
* Deep Hierarchical Parsing
* Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. [Paper]
* Learning Hierarchical Features for Scene Labeling [Paper-ICML12] [Paper-PAMI13]
* Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012.
* Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013.
* University of Cambridge [Web]
* Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” arXiv preprint arXiv:1511.00561, 2015. [Paper]
* Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla “Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding.” arXiv preprint arXiv:1511.02680, 2015. [Paper]
* POSTECH
* Seunghoon Hong, Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation
with Deep Convolutional Neural Network, arXiv:1512.07928 [Paper] [Project Page]
* Princeton
* Fisher Yu, Vladlen Koltun, “Multi-Scale Context Aggregation by Dilated Convolutions”, ICLR 2016, [Paper]
* Univ. of Washington, Allen AI
* Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, “Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing”, ICCV, 2015, [Paper]
* INRIA
* Iasonas Kokkinos, “Pusing the Boundaries of Boundary Detection Using deep Learning”, ICLR 2016, [Paper]
* UCSB
* Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, “Weakly supervised graph based semantic segmentation by learning communities of image-parts”, ICCV, 2015, [Paper]

Visual Attention and Saliency

(from Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.)

• Mr-CNN [Paper]
  
  • Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.
• Learning a Sequential Search for Landmarks [Paper]
  
  • Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.
• Multiple Object Recognition with Visual Attention [Paper]
  
  • Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.
• Recurrent Models of Visual Attention [Paper]
  
  • Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.

Object Recognition

• Weakly-supervised learning with convolutional neural networks [Paper]
  
  • Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.
• FV-CNN [Paper]
  
  • Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.

Understanding CNN

(from Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.)

• Equivariance and Equivalence of Representations [Paper]
  
  • Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015.
• Deep Neural Networks Are Easily Fooled [Paper]
  
  • Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015.
• Understanding Deep Image Representations by Inverting Them [Paper]
  
  • Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.
• Object Detectors Emerge in Deep Scene CNNs [Paper]
  
  • Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015.
• Inverting Convolutional Networks with Convolutional Networks
  
  • Alexey Dosovitskiy, Thomas Brox, Inverting Convolutional Networks with Convolutional Networks, arXiv, 2015. [Paper]
• Visualizing and Understanding CNN
  
  • Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014. [Paper]

Image and Language

Image Captioning

(from Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Description, CVPR, 2015.)

• UCLA / Baidu [Paper]
  
  • Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, Explain Images with Multimodal Recurrent Neural Networks, arXiv:1410.1090.
• Toronto [Paper]
  
  • Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, arXiv:1411.2539.
• Berkeley [Paper]
  
  • Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, arXiv:1411.4389.
• Google [Paper]
  
  • Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, Show and Tell: A Neural Image Caption Generator, arXiv:1411.4555.
• Stanford [Web] [Paper]
  
  • Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Description, CVPR, 2015.
• UML / UT [Paper]
  
  • Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, NAACL-HLT, 2015.
• CMU / Microsoft [Paper-arXiv] [Paper-CVPR]
  
  • Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual Representation for Image Caption Generation, arXiv:1411.5654.
  • Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015
• Microsoft [Paper]
  
  • Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, From Captions to Visual Concepts and Back, CVPR, 2015.
• Univ. Montreal / Univ. Toronto [Web] [Paper]
  
  • Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, arXiv:1502.03044 / ICML 2015
• Idiap / EPFL / Facebook [Paper]
  
  • Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, Phrase-based Image Captioning, arXiv:1502.03671 / ICML 2015
• UCLA / Baidu [Paper]
  
  • Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images, arXiv:1504.06692
• MS + Berkeley
  
  • Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 [Paper]
  • Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, arXiv:1505.01809 [Paper]
• Adelaide [Paper]
  
  • Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, Image Captioning with an Intermediate Attributes Layer, arXiv:1506.01144
• Tilburg [Paper]
  
  • Grzegorz Chrupala, Akos Kadar, Afra Alishahi, Learning language through pictures, arXiv:1506.03694
• Univ. Montreal [Paper]
  
  • Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053
• Cornell [Paper]
  
  • Jack Hessel, Nicolas Savva, Michael J. Wilber, Image Representations and New Domains in Neural Image Captioning, arXiv:1508.02091
• MS + City Univ. of HongKong [Paper]
  
  • Ting Yao, Tao Mei, and Chong-Wah Ngo, “Learning Query and Image Similarities
    with Ranking Canonical Correlation Analysis”, ICCV, 2015

Video Captioning

• Berkeley [Web] [Paper]
  
  • Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR, 2015.
• UT / UML / Berkeley [Paper]
  
  • Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729.
• Microsoft [Paper]
  
  • Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.
• UT / Berkeley / UML [Paper]
  
  • Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence–Video to Text, arXiv:1505.00487.
• Univ. Montreal / Univ. Sherbrooke [Paper]
  
  • Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029
• MPI / Berkeley [Paper]
  
  • Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698
• Univ. Toronto / MIT [Paper]
  
  • Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724
• Univ. Montreal [Paper]
  
  • Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053

Question Answering

(from Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop)

• Virginia Tech / MSR [Web] [Paper]
  
  • Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop.
• MPI / Berkeley [Web] [Paper]
  
  • Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, arXiv:1505.01121.
• Toronto [Paper] [Dataset]
  
  • Mengye Ren, Ryan Kiros, Richard Zemel, Image Question Answering: A Visual Semantic Embedding Model and a New Dataset, arXiv:1505.02074 / ICML 2015 deep learning workshop.
• Baidu / UCLA [Paper] [Dataset]
  
  • Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu, Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering, arXiv:1505.05612.
• POSTECH [Paper] [Project Page]
  
  • Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han, Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction, arXiv:1511.05765
• CMU / Microsoft Research [Paper]
  
  • Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. (2015). Stacked Attention Networks for Image Question Answering. arXiv:1511.02274.
• MetaMind [Paper]
  
  • Xiong, Caiming, Stephen Merity, and Richard Socher. “Dynamic Memory Networks for Visual and Textual Question Answering.” arXiv:1603.01417 (2016).

Other Topics

• Visual Analogy [Paper]
  
  • Scott Reed, Yi Zhang, Yuting Zhang, Honglak Lee, Deep Visual Analogy Making, NIPS, 2015
• Surface Normal Estimation [Paper]
  
  • Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep Networks for Surface Normal Estimation, CVPR, 2015.
• Action Detection [Paper]
  
  • Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR, 2015.
• Crowd Counting [Paper]
  
  • Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.
• 3D Shape Retrieval [Paper]
  
  • Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using Convolutional Neural Networks, CVPR, 2015.
• Generate image [Paper]
  
  • Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, Learning to Generate Chairs with Convolutional Neural Networks, CVPR, 2015.
• Weakly-supervised Classification
  
  • Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, “Auxiliary Image Regularization for Deep CNNs with Noisy Labels”, ICLR 2016, [Paper]
• Weakly-supervised Object Detection
• Generate Image with Adversarial Network
  
  • Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014. [Paper]
  • Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015. [Paper]
  • Lucas Theis, Aäron van den Oord, Matthias Bethge, “A note on the evaluation of generative models”, ICLR 2016. [Paper]
  • Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, “Variationally Auto-Encoded Deep Gaussian Processes”, ICLR 2016. [Paper]
  • Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, “Generating Images from Captions with Attention”, ICLR 2016, [Paper]
  • Jost Tobias Springenberg, “Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks”, ICLR 2016, [Paper]
  • Harrison Edwards, Amos Storkey, “Censoring Representations with an Adversary”, ICLR 2016, [Paper]
  • Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, “Distributional Smoothing with Virtual Adversarial Training”, ICLR 2016, [Paper]
• Artistic Style [Paper] [Code]
  
  • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, A Neural Algorithm of Artistic Style.
• Human Gaze Estimation
  
  • Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling, Appearance-Based Gaze Estimation in the Wild, CVPR, 2015. [Paper] [Website]
• Face Recognition
  
  • Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014. [Paper]
  • Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. [Paper]
  • Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. [Paper]

Courses

• Deep Vision
  
  • [Stanford] CS231n: Convolutional Neural Networks for Visual Recognition
  • [CUHK] ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)
• More Deep Learning
  
  • [Stanford] CS224d: Deep Learning for Natural Language Processing
  • [Oxford] Deep Learning by Prof. Nando de Freitas
  • [NYU] Deep Learning by Prof. Yann LeCun

Books

• Free Online Books
  
  • Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville
  • Neural Networks and Deep Learning by Michael Nielsen
  • Deep Learning Tutorial by LISA lab, University of Montreal

Videos

• Talks
  
  • Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
  • Recent Developments in Deep Learning By Geoff Hinton
  • The Unreasonable Effectiveness of Deep Learning by Yann LeCun
  • Deep Learning of Representations by Yoshua bengio
• Courses
  
  • Deep Learning Course – Nando de Freitas@Oxford

Software

Framework

• Tensorflow: An open source software library for numerical computation using data flow graph by Google [Web]
• Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [Web]
• Caffe: Deep learning framework by the BVLC [Web]
• Theano: Mathematical library in Python, maintained by LISA lab [Web]
  
  • Theano-based deep learning libraries: [Pylearn2], [Blocks], [Keras], [Lasagne]
• MatConvNet: CNNs for MATLAB [Web]

Applications

• Adversarial Training
  
  • Code and hyperparameters for the paper “Generative Adversarial Networks” [Web]
• Understanding and Visualizing
  
  • Source code for “Understanding Deep Image Representations by Inverting Them,” CVPR, 2015. [Web]
• Semantic Segmentation
  
  • Source code for the paper “Rich feature hierarchies for accurate object detection and semantic segmentation,” CVPR, 2014. [Web]
  • Source code for the paper “Fully Convolutional Networks for Semantic Segmentation,” CVPR, 2015. [Web]
• Super-Resolution
  
  • Image Super-Resolution for Anime-Style-Art [Web]
• Edge Detection
  
  • Source code for the paper “DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection,” CVPR, 2015. [Web]

Tutorials

• [CVPR 2014] Tutorial on Deep Learning in Computer Vision
• [CVPR 2015] Applied Deep Learning for Computer Vision with Torch

Blogs

• Deep down the rabbit hole: CVPR 2015 and beyond@Tombone’s Computer Vision Blog
• CVPR recap and where we’re going@Zoya Bylinskii (MIT PhD Student)’s Blog
• Facebook’s AI Painting@Wired
• Inceptionism: Going Deeper into Neural Networks@Google Research

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Awesome Deep Learning

Table of Contents

• Free Online Books

• Courses

• Videos and Lectures

• Papers

• Tutorials

• Researchers

• WebSites

• Datasets

• Frameworks

• Miscellaneous

• Contributing

Free Online Books

1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
2. Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
3. Deep Learning by Microsoft Research (2013)
4. Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
5. neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation
6. An introduction to genetic algorithms
7. Artificial Intelligence: A Modern Approach
8. Deep Learning in Neural Networks: An Overview

Courses

1. Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
2. Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
3. Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
4. Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
5. Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)
6. Deep Learning Course by CILVR lab @ NYU (2014)
7. A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
8. A.I - MIT by Patrick Henry Winston (2010)
9. Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
10. Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2015)
11. Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2016)
12. Deep Learning for Natural Language Processing - Stanford
13. Neural Networks - usherbrooke
14. Machine Learning - Oxford (2014-2015)
15. Deep Learning - Nvidia (2015)
16. [Graduate Summer School: Deep Learning, Feature Learning] (https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
17. Deep Learning - Google by Vincent Vanhoucke and Arpan Chakraborty (2016)

Videos and Lectures

1. How To Create A Mind By Ray Kurzweil
2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
3. Recent Developments in Deep Learning By Geoff Hinton
4. The Unreasonable Effectiveness of Deep Learning by Yann LeCun
5. Deep Learning of Representations by Yoshua bengio
6. Principles of Hierarchical Temporal Memory by Jeff Hawkins
7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
8. Making Sense of the World with Deep Learning By Adam Coates
9. Demystifying Unsupervised Feature Learning By Adam Coates
10. Visual Perception with Deep Learning By Yann LeCun
11. The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
12. The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels
13. Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)
14. Natural Language Processing By Chris Manning in Stanford
15. A beginners Guide to Deep Neural Networks By Natalie Hammel and Lorraine Yurshansky
16. Deep Learning: Intelligence from Big Data by Steve Jurvetson (and panel) at VLAB in Stanford.

Papers

1. ImageNet Classification with Deep Convolutional Neural Networks
2. Using Very Deep Autoencoders for Content Based Image Retrieval
3. Learning Deep Architectures for AI
4. CMU’s list of papers
5. Neural Networks for Named Entity
   Recognition zip
6. Training tricks by YB
7. [Geoff Hinton’s reading list (all papers)] (http://www.cs.toronto.edu/~hinton/deeprefs.html)
8. Supervised Sequence Labelling with Recurrent Neural Networks
9. Statistical Language Models based on Neural Networks
10. Training Recurrent Neural Networks
11. Recursive Deep Learning for Natural Language Processing and Computer Vision
12. Bi-directional RNN
13. LSTM
14. GRU - Gated Recurrent Unit
15. GFRNN . .
16. LSTM: A Search Space Odyssey
17. A Critical Review of Recurrent Neural Networks for Sequence Learning
18. Visualizing and Understanding Recurrent Networks
19. Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures
20. Recurrent Neural Network based Language Model
21. Extensions of Recurrent Neural Network Language Model
22. Recurrent Neural Network based Language Modeling in Meeting Recognition
23. Deep Neural Networks for Acoustic Modeling in Speech Recognition
24. Speech Recognition with Deep Recurrent Neural Networks
25. Reinforcement Learning Neural Turing Machines
26. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
27. Google - Sequence to Sequence Learning with Nneural Networks
28. Memory Networks
29. Policy Learning with Continuous Memory States for Partially Observed Robotic Control
30. Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
31. Neural Turing Machines
32. Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
33. Mastering the Game of Go with Deep Neural Networks and Tree Search

Tutorials

1. UFLDL Tutorial 1
2. UFLDL Tutorial 2
3. Deep Learning for NLP (without Magic)
4. A Deep Learning Tutorial: From Perceptrons to Deep Networks
5. Deep Learning from the Bottom up
6. Theano Tutorial
7. Neural Networks for Matlab
8. Using convolutional neural nets to detect facial keypoints tutorial
9. Torch7 Tutorials
10. [The Best Machine Learning Tutorials On The Web] (https://github.com/josephmisiti/machine-learning-module)
11. VGG Convolutional Neural Networks Practical
12. TensorFlow tutorials
13. More TensorFlow tutorials
14. TensorFlow Python Notebooks
15. Keras and Lasagne Deep Learning Tutorials

Researchers

1. Aaron Courville
2. Abdel-rahman Mohamed
3. Adam Coates
4. Alex Acero
5. Alex Krizhevsky
6. Alexander Ilin
7. Amos Storkey
8. Andrej Karpathy
9. Andrew M. Saxe
10. Andrew Ng
11. Andrew W. Senior
12. Andriy Mnih
13. Ayse Naz Erkan
14. Benjamin Schrauwen
15. Bernardete Ribeiro
16. Bo David Chen
17. Boureau Y-Lan
18. Brian Kingsbury
19. Christopher Manning
20. Clement Farabet
21. Dan Claudiu Cireșan
22. David Reichert
23. Derek Rose
24. Dong Yu
25. Drausin Wulsin
26. Erik M. Schmidt
27. Eugenio Culurciello
28. Frank Seide
29. Galen Andrew
30. Geoffrey Hinton
31. George Dahl
32. Graham Taylor
33. Grégoire Montavon
34. Guido Francisco Montúfar
35. Guillaume Desjardins
36. Hannes Schulz
37. Hélène Paugam-Moisy
38. Honglak Lee
39. Hugo Larochelle
40. Ilya Sutskever
41. Itamar Arel
42. James Martens
43. Jason Morton
44. Jason Weston
45. Jeff Dean
46. Jiquan Mgiam
47. Joseph Turian
48. Joshua Matthew Susskind
49. Jürgen Schmidhuber
50. Justin A. Blanco
51. Koray Kavukcuoglu
52. KyungHyun Cho
53. Li Deng
54. Lucas Theis
55. Ludovic Arnold
56. Marc’Aurelio Ranzato
57. Martin Längkvist
58. Misha Denil
59. Mohammad Norouzi
60. Nando de Freitas
61. Navdeep Jaitly
62. Nicolas Le Roux
63. Nitish Srivastava
64. Noel Lopes
65. Oriol Vinyals
66. Pascal Vincent
67. Patrick Nguyen
68. Pedro Domingos
69. Peggy Series
70. Pierre Sermanet
71. Piotr Mirowski
72. Quoc V. Le
73. Reinhold Scherer
74. Richard Socher
75. Rob Fergus
76. Robert Coop
77. Robert Gens
78. Roger Grosse
79. Ronan Collobert
80. Ruslan Salakhutdinov
81. Sebastian Gerwinn
82. Stéphane Mallat
83. Sven Behnke
84. Tapani Raiko
85. Tara Sainath
86. Tijmen Tieleman
87. Tom Karnowski
88. Tomáš Mikolov
89. Ueli Meier
90. Vincent Vanhoucke
91. Volodymyr Mnih
92. Yann LeCun
93. Yichuan Tang
94. Yoshua Bengio
95. Yotaro Kubo
96. Youzhi (Will) Zou

WebSites

1. deeplearning.net
2. deeplearning.stanford.edu
3. nlp.stanford.edu
4. ai-junkie.com
5. cs.brown.edu/research/ai
6. eecs.umich.edu/ai
7. cs.utexas.edu/users/ai-lab
8. cs.washington.edu/research/ai
9. aiai.ed.ac.uk
10. www-aig.jpl.nasa.gov
11. csail.mit.edu
12. cgi.cse.unsw.edu.au/~aishare
13. cs.rochester.edu/research/ai
14. ai.sri.com
15. isi.edu/AI/isd.htm
16. nrl.navy.mil/itd/aic
17. hips.seas.harvard.edu
18. AI Weekly
19. stat.ucla.edu
20. deeplearning.cs.toronto.edu
21. jeffdonahue.com/lrcn/
22. visualqa.org
23. www.mpi-inf.mpg.de/departments/computer-vision…
24. Deep Learning News

Datasets

1. MNIST Handwritten digits
2. Google House Numbers from street view
3. CIFAR-10 and CIFAR-1004.
4. IMAGENET
5. Tiny Images 80 Million tiny images6.
6. Flickr Data 100 Million Yahoo dataset
7. Berkeley Segmentation Dataset 500
8. UC Irvine Machine Learning Repository
9. Flickr 8k
10. Flickr 30k
11. Microsoft COCO
12. VQA
13. Image QA
14. AT&T Laboratories Cambridge face database
15. AVHRR Pathfinder
16. Air Freight - The Air Freight data set is a ray-traced image sequence along with ground truth segmentation based on textural characteristics. (455 images + GT, each 160x120 pixels). (Formats: PNG)
17. Amsterdam Library of Object Images - ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. (Formats: png)
18. Annotated face, hand, cardiac & meat images - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)
19. Image Analysis and Computer Graphics
20. Brown University Stimuli - A variety of datasets including geons, objects, and “greebles”. Good for testing recognition algorithms. (Formats: pict)
21. CAVIAR video sequences of mall and public space behavior - 90K video frames in 90 sequences of various human activities, with XML ground truth of detection and behavior classification (Formats: MPEG2 & JPEG)
22. Machine Vision Unit
23. CCITT Fax standard images - 8 images (Formats: gif)
24. CMU CIL’s Stereo Data with Ground Truth - 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)
25. CMU PIE Database - A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 different expressions.
26. CMU VASC Image Database - Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)
27. Caltech Image Database - about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)
28. Columbia-Utrecht Reflectance and Texture Database - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)
29. Computational Colour Constancy Data - A dataset oriented towards computational color constancy, but useful for computer vision in general. It includes synthetic data, camera sensor data, and over 700 images. (Formats: tiff)
30. Computational Vision Lab
31. Content-based image retrieval database - 11 sets of color images for testing algorithms for content-based retrieval. Most sets have a description file with names of objects in each image. (Formats: jpg)
32. Efficient Content-based Retrieval Group
33. Densely Sampled View Spheres - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)
34. Computer Science VII (Graphical Systems)
35. Digital Embryos - Digital embryos are novel objects which may be used to develop and test object recognition systems. They have an organic appearance. (Formats: various formats are available on request)
36. Univerity of Minnesota Vision Lab
37. El Salvador Atlas of Gastrointestinal VideoEndoscopy - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)
38. FG-NET Facial Aging Database - Database contains 1002 face images showing subjects at different ages. (Formats: jpg)
39. FVC2000 Fingerprint Databases - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 fingerprints in all).
40. Biometric Systems Lab - University of Bologna
41. Face and Gesture images and image sequences - Several image datasets of faces and gestures that are ground truth annotated for benchmarking
42. German Fingerspelling Database - The database contains 35 gestures and consists of 1400 image sequences that contain gestures of 20 different persons recorded under non-uniform daylight lighting conditions. (Formats: mpg,jpg)
43. Language Processing and Pattern Recognition
44. Groningen Natural Image Database - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)
45. ICG Testhouse sequence - 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
46. Institute of Computer Graphics and Vision
47. IEN Image Library - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)
48. INRIA’s Syntim images database - 15 color image of simple objects (Formats: gif)
49. INRIA
50. INRIA’s Syntim stereo databases - 34 calibrated color stereo pairs (Formats: gif)
51. Image Analysis Laboratory - Images obtained from a variety of imaging modalities – raw CFA images, range images and a host of “medical images”. (Formats: homebrew)
52. Image Analysis Laboratory
53. Image Database - An image database including some textures
54. JAFFE Facial Expression Image Database - The JAFFE database consists of 213 images of Japanese female subjects posing 6 basic facial expressions as well as a neutral pose. Ratings on emotion adjectives are also available, free of charge, for research purposes. (Formats: TIFF Grayscale images.)
55. ATR Research, Kyoto, Japan
56. JISCT Stereo Evaluation - 44 image pairs. These data have been used in an evaluation of stereo analysis, as described in the April 1993 ARPA Image Understanding Workshop paper “The JISCT Stereo Evaluation” by R.C.Bolles, H.H.Baker, and M.J.Hannah, 263–274 (Formats: SSI)
57. MIT Vision Texture - Image archive (100+ images) (Formats: ppm)
58. MIT face images and more - hundreds of images (Formats: homebrew)
59. Machine Vision - Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)
60. Mammography Image Databases - 100 or more images of mammograms with ground truth. Additional images available by request, and links to several other mammography databases are provided. (Formats: homebrew)
61. ftp://ftp.cps.msu.edu/pub/prip - many images (Formats: unknown)
62. Middlebury Stereo Data Sets with Ground Truth - Six multi-frame stereo data sets of scenes containing planar regions. Each data set contains 9 color images and subpixel-accuracy ground-truth data. (Formats: ppm)
63. Middlebury Stereo Vision Research Page - Middlebury College
64. Modis Airborne simulator, Gallery and data set - High Altitude Imagery from around the world for environmental modeling in support of NASA EOS program (Formats: JPG and HDF)
65. NIST Fingerprint and handwriting - datasets - thousands of images (Formats: unknown)
66. NIST Fingerprint data - compressed multipart uuencoded tar file
67. NLM HyperDoc Visible Human Project - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)
68. National Design Repository - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineerign designs. (Formats: gif,vrml,wrl,stp,sat)
69. Geometric & Intelligent Computing Laboratory
70. OSU (MSU) 3D Object Model Database - several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)
71. OSU (MSU/WSU) Range Image Database - Hundreds of real and synthetic images (Formats: gif, homebrew)
72. OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences - Over 1000 range images, 3D object models, still images and motion sequences (Formats: gif, ppm, vrml, homebrew)
73. Signal Analysis and Machine Perception Laboratory
74. Otago Optical Flow Evaluation Sequences - Synthetic and real sequences with machine-readable ground truth optical flow fields, plus tools to generate ground truth for new sequences. (Formats: ppm,tif,homebrew)
75. Vision Research Group
76. ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/ - Real and synthetic image sequences used for testing a Particle Image Velocimetry application. These images may be used for the test of optical flow and image matching algorithms. (Formats: pgm (raw))
77. LIMSI-CNRS/CHM/IMM/vision
78. LIMSI-CNRS
79. Photometric 3D Surface Texture Database - This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 textures, 1680 images). (Formats: TIFF)
80. SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)
81. Computer Vision Group
82. Sequences for Flow Based Reconstruction - synthetic sequence for testing structure from motion algorithms (Formats: pgm)
83. Stereo Images with Ground Truth Disparity and Occlusion - a small set of synthetic images of a hallway with varying amounts of noise added. Use these images to benchmark your stereo algorithm. (Formats: raw, viff (khoros), or tiff)
84. Stuttgart Range Image Database - A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)
85. Department Image Understanding
86. The AR Face Database - Contains over 4,000 color images corresponding to 126 people’s faces (70 men and 56 women). Frontal views with variations in facial expressions, illumination, and occlusions. (Formats: RAW (RGB 24-bit))
87. Purdue Robot Vision Lab
88. The MIT-CSAIL Database of Objects and Scenes - Database for testing multiclass object detection and scene recognition algorithms. Over 72,000 images with 2873 annotated frames. More than 50 annotated object classes. (Formats: jpg)
89. The RVL SPEC-DB (SPECularity DataBase) - A collection of over 300 real images of 100 objects taken under three different illuminaiton conditions (Diffuse/Ambient/Directed). – Use these images to test algorithms for detecting and compensating specular highlights in color images. (Formats: TIFF )
90. Robot Vision Laboratory
91. The Xm2vts database - The XM2VTSDB contains four digital recordings of 295 people taken over a period of four months. This database contains both image and video data of faces.
92. Centre for Vision, Speech and Signal Processing
93. Traffic Image Sequences and ‘Marbled Block’ Sequence - thousands of frames of digitized traffic image sequences as well as the ‘Marbled Block’ sequence (grayscale images) (Formats: GIF)
94. IAKS/KOGS
95. U Bern Face images - hundreds of images (Formats: Sun rasterfile)
96. U Michigan textures (Formats: compressed raw)
97. U Oulu wood and knots database - Includes classifications - 1000+ color images (Formats: ppm)
98. UCID - an Uncompressed Colour Image Database - a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)
99. UMass Vision Image Archive - Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)
100. UNC’s 3D image database - many images (Formats: GIF)
101. USF Range Image Data with Segmentation Ground Truth - 80 image sets (Formats: Sun rasterimage)
102. University of Oulu Physics-based Face Database - contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral reflectance measurements of each person.
103. Machine Vision and Media Processing Unit
104. University of Oulu Texture Database - Database of 320 surface textures, each captured under three illuminants, six spatial resolutions and nine rotation angles. A set of test suites is also provided so that texture segmentation, classification, and retrieval algorithms can be tested in a standard manner. (Formats: bmp, ras, xv)
105. Machine Vision Group
106. Usenix face database - Thousands of face images from many different sites (circa 994)
107. View Sphere Database - Images of 8 objects seen from many different view points. The view sphere is sampled using a geodesic with 172 images/sphere. Two sets for training and testing are available. (Formats: ppm)
108. PRIMA, GRAVIR
109. Vision-list Imagery Archive - Many images, many formats
110. Wiry Object Recognition Database - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. (Formats: jpg)
111. 3D Vision Group
112. Yale Face Database - 165 images (15 individuals) with different lighting, expression, and occlusion configurations.
113. Yale Face Database B - 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). (Formats: PGM)
114. Center for Computational Vision and Control

Frameworks

1. Caffe
2. Torch7
3. Theano
4. cuda-convnet
5. convetjs
6. Ccv
7. NuPIC
8. DeepLearning4J
9. Brain
10. DeepLearnToolbox
11. Deepnet
12. Deeppy
13. JavaNN
14. hebel
15. Mocha.jl
16. OpenDL
17. cuDNN
18. MGL
19. KUnet.jl
20. Nvidia DIGITS - a web app based on Caffe
21. Neon - Python based Deep Learning Framework
22. Keras - Theano based Deep Learning Library
23. Chainer - A flexible framework of neural networks for deep learning
24. RNNLM Toolkit
25. RNNLIB - A recurrent neural network library
26. char-rnn
27. MatConvNet: CNNs for MATLAB
28. Minerva - a fast and flexible tool for deep learning on multi-GPU
29. Brainstorm - Fast, flexible and fun neural networks.
30. Tensorflow - Open source software library for numerical computation using data flow graphs
31. DMTK - Microsoft Distributed Machine Learning Tookit
32. Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)
33. MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework
34. Veles - Samsung Distributed machine learning platform
35. Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework
36. Apache SINGA - A General Distributed Deep Learning Platform

Miscellaneous

1. Google Plus - Deep Learning Community
2. Caffe Webinar
3. 100 Best Github Resources in Github for DL
4. Word2Vec
5. Caffe DockerFile
6. TorontoDeepLEarning convnet
7. gfx.js
8. Torch7 Cheat sheet
9. [Misc from MIT’s ‘Advanced Natural Language Processing’ course] (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
10. Misc from MIT’s ‘Machine Learning’ course
11. Misc from MIT’s ‘Networks for Learning: Regression and Classification’ course
12. Misc from MIT’s ‘Neural Coding and Perception of Sound’ course
13. Implementing a Distributed Deep Learning Network over Spark
14. A chess AI that learns to play chess using deep learning.
15. [Reproducing the results of “Playing Atari with Deep Reinforcement Learning” by DeepMind] (https://github.com/kristjankorjus/Replicating-DeepMind)
16. Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps
17. The original code from the DeepMind article + tweaks
18. Google deepdream - Neural Network art
19. An efficient, batched LSTM.
20. A recurrent neural network designed to generate classical music.
21. Memory Networks Implementations - Facebook
22. Face recognition with Google’s FaceNet deep neural network.
23. Basic digit recognition neural network
24. Emotion Recognition API Demo - Microsoft
25. Proof of concept for loading Caffe models in TensorFlow
26. YOLO: Real-Time Object Detection
27. AlphaGo - A replication of DeepMind’s 2016 Nature publication, “Mastering the game of Go with deep neural networks and tree search”

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Contributing

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.

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License

To the extent possible under law, Christos Christofidis has waived all copyright and related or neighboring rights to this work.