Deep Learning 深度学习 专知荟萃

白皮书/报告

1. 《深度学习技术选型白皮书（2018年）》中国人工智能产业发展联盟，[http://www.caict.ac.cn/kxyj/qwfb/bps/201810/P020181017317431141487.pdf]
2. 《使用 Dell EMC Isilon 实现深度学习 技术白皮书》，Dell，[https://www.delltechnologies.com/asset/zh-cn/products/storage/industry-market/h17361_wp_deep_learning_and_dell_emc_isilon.pdf]

入门学习

1. 《一天搞懂深度学习》台大 李宏毅 300页PPT

   • PPT
   • 视频：[https://www.youtube.com/watch?v=ZrEsLwCjdxY]
     • [https://pan.baidu.com/s/1i4DhD7R]

2. Deep Learning（深度学习）学习笔记整理系列之（1-8）

   • [http://blog.csdn.net/zouxy09/article/details/8775360]

3. 深层学习为何要“Deep”（上，下）

   • 上篇
   • 下篇

4. 《神经网络与深度学习》 作者：邱锡鹏 中文图书 2017

   • [https://nndl.github.io/]

5. 深度学习基础 206页PPT 邱锡鹏 复旦大学 2017年8月17日

   • [http://nlp.fudan.edu.cn/xpqiu/slides/20170817-CIPS-ATT-DL.pdf]

6. 《Neural Networks and Deep Learning》 By Michael Nielsen / Aug 2017
   - 原文：[http://neuralnetworksanddeeplearning.com/index.html]

   • 中文翻译：[http://www.liuxiao.org/wp-content/uploads/2016/10/nndl-ebook.pdf]
   • 源码：[https://github.com/mnielsen/neural-networks-and-deep-learning]

7. 李宏毅机器学习视频和笔记。

   • 笔记
   • 视频

8. 吴恩达（AndrewNg）深度学习视频和笔记

   • 笔记
   • 黄海广博士笔记
   • 视频
   • 作业

9. 动手学深度学习pytorch版

10. 北京交通大学 丛润民 讲课PPT及视频《深度学习》平台课，面向硕士生，共32学时

综述

1. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature 521.7553 (2015): 436-444. (Three Giants' Survey)

2. Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012.

3. Deep learning in neural networks: An overview(2015)

   • [https://www.sciencedirect.com/science/article/pii/S0893608014002135]

4. Text summarization using unsupervised deep learning(2017 - Elsevier)

   • [https://www.sciencedirect.com/science/article/pii/S0957417416305486]

5. 张荣，李伟平，莫同，深度学习研究综述, 信息与控制，vol 47（4）,2018.

6. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electronic Markets, 2021, 31(3): 685-695.

7. Thompson N C, Greenewald K, Lee K, et al. The computational limits of deep learning. arXiv preprint arXiv:2007.05558, 2020.

8. Wani M A, Bhat F A, Afzal S, et al. Advances in deep learning. Springer, 2020.

9. Zhang Z, Cui P, Zhu W. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 2020.

进阶文章

Deep Belief Network(DBN)(Milestone of Deep Learning Eve)

• Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554.
  • [http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf] Deep Learning Eve
• Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507.
  • [http://www.cs.toronto.edu/~hinton/science.pdf] Milestone, Show the promise of deep learning
• Deep belief network based deterministic and probabilistic wind speed forecasting approach(2016)
  • [https://www.sciencedirect.com/science/article/pii/S0306261916312053]

ImageNet Evolution（Deep Learning broke out from here）

• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.

  • [http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf] AlexNet, Deep Learning Breakthrough

• Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).

  • [https://arxiv.org/pdf/1409.1556.pdf] VGGNet,Neural Networks become very deep!

• Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

  • [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf] GoogLeNet

• He, Kaiming, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1512.03385 (2015).

  • [https://arxiv.org/pdf/1512.03385.pdf] ResNet,Very very deep networks, CVPR best paper

• Yang You, Zhao Zhang, Cho-Jui Hsiesh, James Demmel, Kurt Keutzer "ImageNet Training in Minutes"(2018)

  • [https://dl.acm.org/citation.cfm?id=3225069]

Model

• Hinton, Geoffrey E., et al. "Improving neural networks by preventing co-adaptation of feature detectors." arXiv preprint arXiv:1207.0580 (2012).
  • [https://arxiv.org/pdf/1207.0580.pdf] Dropout
• Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958.
  • [http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf?utm_content=buffer79b43&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer]
• Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015). [http://arxiv.org/pdf/1502.03167] An outstanding Work in 2015
• Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. "Layer normalization." arXiv preprint arXiv:1607.06450 (2016).
  • [https://arxiv.org/pdf/1607.06450.pdf?utm_source=sciontist.com&utm_medium=refer&utm_campaign=promote] Update of Batch Normalization
• Courbariaux, Matthieu, et al. "Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1."
  • [https://pdfs.semanticscholar.org/f832/b16cb367802609d91d400085eb87d630212a.pdf] New Model,Fast
• Jaderberg, Max, et al. "Decoupled neural interfaces using synthetic gradients." arXiv preprint arXiv:1608.05343 (2016).
  • [https://arxiv.org/pdf/1608.05343] Innovation of Training Method,Amazing Work
• Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. "Net2net: Accelerating learning via knowledge transfer." arXiv preprint arXiv:1511.05641 (2015).
  • [https://arxiv.org/abs/1511.05641] Modify previously trained network to reduce training epochs
• Wei, Tao, et al. "Network Morphism." arXiv preprint arXiv:1603.01670 (2016).
  • [https://arxiv.org/abs/1603.01670] Modify previously trained network to reduce training epochs

Optimizations

• Sutskever, Ilya, et al. "On the importance of initialization and momentum in deep learning." ICML (3) 28 (2013): 1139-1147.
  • [http://www.jmlr.org/proceedings/papers/v28/sutskever13.pdf] Momentum optimizer
• Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
  • [http://arxiv.org/pdf/1412.6980] Maybe used most often currently
• Andrychowicz, Marcin, et al. "Learning to learn by gradient descent by gradient descent." arXiv preprint arXiv:1606.04474 (2016).
  • [https://arxiv.org/pdf/1606.04474] Neural Optimizer,Amazing Work
• Han, Song, Huizi Mao, and William J. Dally. "Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding." CoRR, abs/1510.00149 2 (2015).
  • [https://pdfs.semanticscholar.org/5b6c/9dda1d88095fa4aac1507348e498a1f2e863.pdf] ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup
• Iandola, Forrest N., et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." arXiv preprint arXiv:1602.07360 (2016).
  • [http://arxiv.org/pdf/1602.07360] Also a new direction to optimize NN,DeePhi Tech Startup
• Ravi N, Reizenstein J, Novotny D, et al. Accelerating 3d deep learning with pytorch3d. arXiv preprint arXiv:2007.08501, 2020.

Unsupervised Learning / Deep Generative Model

• Le, Quoc V. "Building high-level features using large scale unsupervised learning." 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013.

  • [http://arxiv.org/pdf/1112.6209.pdf] &embedMilestone, Andrew Ng, Google Brain Project, Cat

• Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).

  • [http://arxiv.org/pdf/1312.6114] VAE

• Goodfellow, Ian, et al. "Generative adversarial nets." Advances in Neural Information Processing Systems. 2014.

  • [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf] GAN,super cool idea

• Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).

  • [http://arxiv.org/pdf/1511.06434] DCGAN

• Gregor, Karol, et al. "DRAW: A recurrent neural network for image generation." arXiv preprint arXiv:1502.04623 (2015).

  • [http://jmlr.org/proceedings/papers/v37/gregor15.pdf] VAE with attention, outstanding work

• Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. "Pixel recurrent neural networks." arXiv preprint arXiv:1601.06759 (2016).

  • [http://arxiv.org/pdf/1601.06759] PixelRNN

• Oord, Aaron van den, et al. "Conditional image generation with PixelCNN decoders." arXiv preprint arXiv:1606.05328 (2016).

  • [https://arxiv.org/pdf/1606.05328] PixelCNN

• Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient"

  • [https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewPaper/14344]

• Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe "Unsupervised Learning of Depth and Ego-Motion From Video"(CVPR2017)

  • [https://arxiv.org/abs/1704.07813]

• Ziliang Chen, Jingyu Zhuang, Xiaodan Liang, Liang Lin "Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks"(CVPR2019)

  • [https://arxiv.org/abs/1907.03389]

RNN / Sequence-to-Sequence Model

• Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013).
  • [http://arxiv.org/pdf/1308.0850] LSTM, very nice generating result, show the power of RNN
• Cho, Kyunghyun, et al. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014).
  • [http://arxiv.org/pdf/1406.1078] First Seq-to-Seq Paper
• Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural information processing systems. 2014.
  • [https://arxiv.org/pdf/1409.3215.pdf] Outstanding Work
• Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. "Neural Machine Translation by Jointly Learning to Align and Translate." arXiv preprint arXiv:1409.0473 (2014).
  • [https://arxiv.org/pdf/1409.0473v7.pdf]
• Vinyals, Oriol, and Quoc Le. "A neural conversational model." arXiv preprint arXiv:1506.05869 (2015).
  • [http://arxiv.org/pdf/1506.05869.pdf%20]
  • [http://arxiv.org/pdf/1506.05869.pdf] Seq-to-Seq on Chatbot
• Kecheng Zheng, Zheng-jun Zha, Wei Wei "Abstract Reasoning with Distracting Features "(NeurIPS2019)
  • [https://arxiv.org/abs/1912.00569]

Neural Turing Machine

1. Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural turing machines." arXiv preprint arXiv:1410.5401 (2014).
   • [http://arxiv.org/pdf/1410.5401.pdf] Basic Prototype of Future Computer
2. Zaremba, Wojciech, and Ilya Sutskever. "Reinforcement learning neural Turing machines." arXiv preprint arXiv:1505.00521 362 (2015).
   • [https://pdfs.semanticscholar.org/f10e/071292d593fef939e6ef4a59baf0bb3a6c2b.pdf]

• Weston, Jason, Sumit Chopra, and Antoine Bordes. "Memory networks." arXiv preprint arXiv:1410.3916 (2014).
  • [http://arxiv.org/pdf/1410.3916 ]
• Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." Advances in neural information processing systems. 2015.
  • [http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf]
• Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." Advances in Neural Information Processing Systems. 2015.
  • [http://papers.nips.cc/paper/5866-pointer-networks.pdf]
• Graves, Alex, et al. "Hybrid computing using a neural network with dynamic external memory." Nature (2016).
  • [https://www.dropbox.com/s/0a40xi702grx3dq/2016-graves.pdf] Milestone,combine above papers' ideas
• Caglar Gulcehre, Sarath Chandar, Kyunghyun Cho, Yoshua Bengio "Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes"(Neural computation2018)
  • [https://arxiv.org/abs/1607.00036]

Deep Reinforcement Learning

• Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
  • [http://arxiv.org/pdf/1312.5602.pdf] First Paper named deep reinforcement learning
• Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533.
  • [https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf] Milestone
• Wang, Ziyu, Nando de Freitas, and Marc Lanctot. "Dueling network architectures for deep reinforcement learning." arXiv preprint arXiv:1511.06581 (2015).
  • [http://arxiv.org/pdf/1511.06581] ICLR best paper,great idea
• Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning." arXiv preprint arXiv:1602.01783 (2016).
  • [http://arxiv.org/pdf/1602.01783] State-of-the-art method
• Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).
  • [http://arxiv.org/pdf/1509.02971] DDPG
• Gu, Shixiang, et al. "Continuous Deep Q-Learning with Model-based Acceleration." arXiv preprint arXiv:1603.00748 (2016). [http://arxiv.org/pdf/1603.00748) (NAF) ]
• Schulman, John, et al. "Trust region policy optimization." CoRR, abs/1502.05477 (2015).
  • [http://www.jmlr.org/proceedings/papers/v37/schulman15.pdf] TRPO
• Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489.
  • [https://hci.iwr.uni-heidelberg.de/system/files/private/downloads/36349047/report_florian_brunner.pdf] AlphaGo
• Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver "Rainbow: Combining Improvements in Deep Reinforcement Learning"(AAAI2018)
  • [https://arxiv.org/abs/1710.02298]

Deep Transfer Learning / Lifelong Learning / especially for RL

• Bengio, Yoshua. "Deep Learning of Representations for Unsupervised and Transfer Learning." ICML Unsupervised and Transfer Learning 27 (2012): 17-36.
  • [http://www.jmlr.org/proceedings/papers/v27/bengio12a/bengio12a.pdf] A Tutorial
• Silver, Daniel L., Qiang Yang, and Lianghao Li. "Lifelong Machine Learning Systems: Beyond Learning Algorithms." AAAI Spring Symposium: Lifelong Machine Learning. 2013.
  • [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.7800&rep=rep1&type=pdf] A brief discussion about lifelong learning
• Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015).
  • [http://arxiv.org/pdf/1503.02531) (Godfather's Work) ]
• Rusu, Andrei A., et al. "Policy distillation." arXiv preprint arXiv:1511.06295 (2015).
  • [http://arxiv.org/pdf/1511.06295] RL domain
• Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. "Actor-mimic: Deep multitask and transfer reinforcement learning." arXiv preprint arXiv:1511.06342 (2015).
  • [http://arxiv.org/pdf/1511.06342] RL domain
• Rusu, Andrei A., et al. "Progressive neural networks." arXiv preprint arXiv:1606.04671 (2016).
  • [https://arxiv.org/pdf/1606.04671] Outstanding Work, A novel idea
• German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, Stefan Wermter "Continual Lifelong Learning with Neural Networks: A Review"(2019 - Elsevier)
  • [https://arxiv.org/abs/1802.07569]

One Shot Deep Learning

• Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. "Human-level concept learning through probabilistic program induction." Science 350.6266 (2015): 1332-1338.

  • [http://clm.utexas.edu/compjclub/wp-content/uploads/2016/02/lake2015.pdf] No Deep Learning,but worth reading

• Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. "Siamese Neural Networks for One-shot Image Recognition."(2015)

  • [http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf]

• Santoro, Adam, et al. "One-shot Learning with Memory-Augmented Neural Networks." arXiv preprint arXiv:1605.06065 (2016).

  • [http://arxiv.org/pdf/1605.06065] A basic step to one shot learning

• Vinyals, Oriol, et al. "Matching Networks for One Shot Learning." arXiv preprint arXiv:1606.04080 (2016).

  • [https://arxiv.org/pdf/1606.04080]

• Hariharan, Bharath, and Ross Girshick. "Low-shot visual object recognition." arXiv preprint arXiv:1606.02819 (2016).

  • [http://arxiv.org/pdf/1606.02819] A step to large data

• Matthew Shunshi Zhang, Bradly Stadie "One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation"(2019)

  • [https://arxiv.org/abs/1912.00120]

NLP(Natural Language Processing)

• Antoine Bordes, et al. "Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing." AISTATS(2012)
  • [https://www.hds.utc.fr/~bordesan/dokuwiki/lib/exe/fetch.php?id=en%3Apubli&cache=cache&media=en:bordes12aistats.pdf]
• Mikolov, et al. "Distributed representations of words and phrases and their compositionality." ANIPS(2013): 3111-3119
  • [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf] word2vec
• Sutskever, et al. "“Sequence to sequence learning with neural networks." ANIPS(2014)
  • [http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf]
• Ankit Kumar, et al. "“Ask Me Anything: Dynamic Memory Networks for Natural Language Processing." arXiv preprint arXiv:1506.07285(2015)
  • [https://arxiv.org/abs/1506.07285 ]
• Yoon Kim, et al. "Character-Aware Neural Language Models." NIPS(2015) arXiv preprint arXiv:1508.06615(2015)
  • [https://arxiv.org/abs/1508.06615) ]
• Jason Weston, et al. "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks." arXiv preprint arXiv:1502.05698(2015) - [https://arxiv.org/abs/1502.05698) (bAbI tasks) ]
• Karl Moritz Hermann, et al. "Teaching Machines to Read and Comprehend." arXiv preprint arXiv:1506.03340(2015) - [https://arxiv.org/abs/1506.03340] CNN/DailyMail cloze style questions
• Alexis Conneau, et al. "Very Deep Convolutional Networks for Natural Language Processing." arXiv preprint arXiv:1606.01781(2016) - [https://arxiv.org/abs/1606.01781] state-of-the-art in text classification
• Armand Joulin, et al. "Bag of Tricks for Efficient Text Classification." arXiv preprint arXiv:1607.01759(2016) - [https://arxiv.org/abs/1607.01759] slightly worse than state-of-the-art, but a lot faster
• Souvick Ghosh, Satanu Ghosh "Exploring the Ideal Depth of Neural Network when Predicting Question Deletion on Community Question Answering"(2019)
  • [https://arxiv.org/abs/1912.03585]
• Makoto Nakatsuji, Sohei Okui "Conclusion-Supplement Answer Generation for Non-Factoid Questions"(AAAI2020)
  • [https://arxiv.org/abs/1912.00864]

Object Detection

• Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. "Deep neural networks for object detection." Advances in Neural Information Processing Systems. 2013.
  • [http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf]
• Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
  • [http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf] RCNN
• He, Kaiming, et al. "Spatial pyramid pooling in deep convolutional networks for visual recognition." European Conference on Computer Vision. Springer International Publishing, 2014.
  • [http://arxiv.org/pdf/1406.4729] SPPNet
• Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on Computer Vision. 2015.
  • [https://pdfs.semanticscholar.org/8f67/64a59f0d17081f2a2a9d06f4ed1cdea1a0ad.pdf]
• Ren, Shaoqing, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015.
  • [http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf]
• Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
  • [http://homes.cs.washington.edu/~ali/papers/YOLO.pdf] YOLO,Oustanding Work, really practical)
• Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." arXiv preprint arXiv:1512.02325 (2015).
  • [http://arxiv.org/pdf/1512.02325]
• Dai, Jifeng, et al. "R-FCN: Object Detection viaRegion-based Fully Convolutional Networks." arXiv preprint arXiv:1605.06409 (2016).
  • [https://arxiv.org/abs/1605.06409]
• He, Gkioxari, et al. "Mask R-CNN" ICCV2017 Best Paper(2017).
  • [https://arxiv.org/abs/1703.06870 ]
• Mohamed Ramzy, Hazem Rashed, Ahmad El Sallab, Senthil Yogamani "RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving"(NeurIPS2019)
  • [https://arxiv.org/abs/1912.00438]

Visual Tracking

• . Wang, Naiyan, and Dit-Yan Yeung. "Learning a deep compact image representation for visual tracking." Advances in neural information processing systems. 2013.
  • [http://papers.nips.cc/paper/5192-learning-a-deep-compact-image-representation-for-visual-tracking.pdf] First Paper to do visual tracking using Deep Learning,DLT Tracker
• Wang, Naiyan, et al. "Transferring rich feature hierarchies for robust visual tracking." arXiv preprint arXiv:1501.04587 (2015).
  • [http://arxiv.org/pdf/1501.04587] SO-DLT
• Wang, Lijun, et al. "Visual tracking with fully convolutional networks." Proceedings of the IEEE International Conference on Computer Vision. 2015.
  • [http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wang_Visual_Tracking_With_ICCV_2015_paper.pdf] FCNT
• Held, David, Sebastian Thrun, and Silvio Savarese. "Learning to Track at 100 FPS with Deep Regression Networks." arXiv preprint arXiv:1604.01802 (2016).
  • [http://arxiv.org/pdf/1604.01802] GOTURN,Really fast as a deep learning method,but still far behind un-deep-learning methods
• Bertinetto, Luca, et al. "Fully-Convolutional Siamese Networks for Object Tracking." arXiv preprint arXiv:1606.09549 (2016).
  • [https://arxiv.org/pdf/1606.09549]SiameseFC,New state-of-the-art for real-time object tracking
• Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. "Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." ECCV (2016)
  • [http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/C-COT_ECCV16.pdf] C-COT
• Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." arXiv preprint arXiv:1608.07242 (2016).
  • [https://arxiv.org/pdf/1608.07242) (VOT2016 Winner,TCNN)
• Qintao Hu, Lijun Zhou, Xiaoxiao Wang, Yao Mao, Jianlin Zhang, Qixiang Ye "SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking"(AAAI2020)
  • [https://arxiv.org/abs/1912.00597]

Image Caption

• Farhadi,Ali,etal. "Every picture tells a story: Generating sentences from images". In Computer VisionECCV 2010. Springer Berlin Heidelberg:15-29, 2010.
  • [https://www.cs.cmu.edu/~afarhadi/papers/sentence.pdf]
• Kulkarni, Girish, et al. "Baby talk: Understanding and generating image descriptions". In Proceedings of the 24th CVPR, 2011.
  • [http://tamaraberg.com/papers/generation_cvpr11.pdf]
• Vinyals, Oriol, et al. "Show and tell: A neural image caption generator". In arXiv preprint arXiv:1411.4555, 2014.
  • [https://arxiv.org/pdf/1411.4555.pdf]
• Donahue, Jeff, et al. "Long-term recurrent convolutional networks for visual recognition and description". In arXiv preprint arXiv:1411.4389 ,2014.
  • [https://arxiv.org/pdf/1411.4389.pdf]
• Karpathy, Andrej, and Li Fei-Fei. "Deep visual-semantic alignments for generating image descriptions". In arXiv preprint arXiv:1412.2306, 2014.
  • [https://cs.stanford.edu/people/karpathy/cvpr2015.pdf]
• Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. "Deep fragment embeddings for bidirectional image sentence mapping". In Advances in neural information processing systems, 2014.
  • [https://arxiv.org/pdf/1406.5679v1.pdf]
• Fang, Hao, et al. "From captions to visual concepts and back". In arXiv preprint arXiv:1411.4952, 2014.
  • [https://arxiv.org/pdf/1411.4952v3.pdf]
• Chen, Xinlei, and C. Lawrence Zitnick. "Learning a recurrent visual representation for image caption generation". In arXiv preprint arXiv:1411.5654, 2014.
  • [https://arxiv.org/pdf/1411.5654v1.pdf]
• Mao, Junhua, et al. "Deep captioning with multimodal recurrent neural networks (m-rnn)". In arXiv preprint arXiv:1412.6632, 2014.
  • [https://arxiv.org/pdf/1412.6632v5.pdf]
• Xu, Kelvin, et al. "Show, attend and tell: Neural image caption generation with visual attention". In arXiv preprint arXiv:1502.03044, 2015.
  • [https://arxiv.org/pdf/1502.03044v3.pdf]
• Zheng-cong Fei "Better Understanding Hierarchical Visual Relationship for Image Caption"(NeurIPS2019)
  • [https://arxiv.org/abs/1912.01881]

Machine Translation

• Luong, Minh-Thang, et al. "Addressing the rare word problem in neural machine translation." arXiv preprint arXiv:1410.8206 (2014).

  • [http://arxiv.org/pdf/1410.8206]

• Sennrich, et al. "Neural Machine Translation of Rare Words with Subword Units". In arXiv preprint arXiv:1508.07909, 2015.

  • [https://arxiv.org/pdf/1508.07909.pdf]

• Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Effective approaches to attention-based neural machine translation." arXiv preprint arXiv:1508.04025 (2015).

  • [http://arxiv.org/pdf/1508.04025]

• Chung, et al. "A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation". In arXiv preprint arXiv:1603.06147, 2016.

  • [https://arxiv.org/pdf/1603.06147.pdf]

• Lee, et al. "Fully Character-Level Neural Machine Translation without Explicit Segmentation". In arXiv preprint arXiv:1610.03017, 2016.

  • [https://arxiv.org/pdf/1610.03017.pdf]

• Wu, Schuster, Chen, Le, et al. "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation". In arXiv preprint arXiv:1609.08144v2, 2016.

  • [https://arxiv.org/pdf/1609.08144v2.pdf] Milestone

• Surabhi Punjabi, Harish Arsikere, Sri Garimella "Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition"(2019)

  • [https://arxiv.org/abs/1912.00958]

Robotics

• Koutník, Jan, et al. "Evolving large-scale neural networks for vision-based reinforcement learning." Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, 2013.
  • [http://repository.supsi.ch/4550/1/koutnik2013gecco.pdf]
• Levine, Sergey, et al. "End-to-end training of deep visuomotor policies." Journal of Machine Learning Research 17.39 (2016): 1-40.
  • [http://www.jmlr.org/papers/volume17/15-522/15-522.pdf]
• Pinto, Lerrel, and Abhinav Gupta. "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." arXiv preprint arXiv:1509.06825 (2015).
  • [http://arxiv.org/pdf/1509.06825]
• Levine, Sergey, et al. "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." arXiv preprint arXiv:1603.02199 (2016).
  • [http://arxiv.org/pdf/1603.02199]
• Zhu, Yuke, et al. "Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning." arXiv preprint arXiv:1609.05143 (2016).
  • [https://arxiv.org/pdf/1609.05143]
• Yahya, Ali, et al. "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search." arXiv preprint arXiv:1610.00673 (2016).
  • [https://arxiv.org/pdf/1610.00673]
• Gu, Shixiang, et al. "Deep Reinforcement Learning for Robotic Manipulation." arXiv preprint arXiv:1610.00633 (2016).
  • [https://arxiv.org/pdf/1610.00633]
• A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell."Sim-to-Real Robot Learning from Pixels with Progressive Nets." arXiv preprint arXiv:1610.04286 (2016).
  • [https://arxiv.org/pdf/1610.04286.pdf]
• Mirowski, Piotr, et al. "Learning to navigate in complex environments." arXiv preprint arXiv:1611.03673 (2016).
  • [https://arxiv.org/pdf/1611.03673]
• Zehui Meng, Qi Heng Ho, Zefan Huang, Hongliang Guo, Marcelo H. Ang Jr., Daniela Rus "Online Multi-Target Tracking for Maneuvering Vehicles in Dynamic Road Context"(2019)
  • [https://arxiv.org/abs/1912.00603]

Object Segmentation

• J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015.
  • [https://arxiv.org/pdf/1411.4038v2.pdf]
• L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. "Semantic image segmentation with deep convolutional nets and fully connected crfs." In ICLR, 2015.
  • [https://arxiv.org/pdf/1606.00915v1.pdf]
• Pinheiro, P.O., Collobert, R., Dollar, P. "Learning to segment object candidates." In: NIPS. 2015.
  • [https://arxiv.org/pdf/1506.06204v2.pdf]
• Dai, J., He, K., Sun, J. "Instance-aware semantic segmentation via multi-task network cascades." in CVPR. 2016
  • [https://arxiv.org/pdf/1512.04412v1.pdf]
• Dai, J., He, K., Sun, J. "Instance-sensitive Fully Convolutional Networks." arXiv preprint arXiv:1603.08678 (2016).
  • [https://arxiv.org/pdf/1603.08678v1.pdf]
• Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen, Jordi Pont-Tuset,Laura Leal-Taixe, Daniel Cremers, and Luc Van Gool "Video Object Segmentation without Temporal Information"(IEEE-TPAMI2018)
  • [https://ieeexplore.ieee.org/abstract/document/8362936]
• Fan Yang, Cheng Lv, Yandong Guo, Longin Jan Latecki, Haibin Ling"Dually Supervised Feature Pyramid for Object Detection and Segmentation"(2019)
  • [https://arxiv.org/abs/1912.03730]
• Zehui Meng, Qi Heng Ho, Zefan Huang, Hongliang Guo, Marcelo H. Ang Jr., Daniela Rus "Online Multi-Target Tracking for Maneuvering Vehicles in Dynamic Road Context"(2020)
  • [https://arxiv.org/abs/1912.00603]

Medical Image Analysis

• Medical Image Processing, Analysis and Visualization in clinical research

  • [https://ieeexplore.ieee.org/abstract/document/941749]

• A survey on deep learning in medical image analysis （2017)

  • [https://www.sciencedirect.com/science/article/pii/S1361841517301135)

• Deep Learning in Medical Image Analysis(2017)

  • [https://www.annualreviews.org/doi/abs/10.1146/annurev-bioeng-071516-044442]

• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes(2018)

  • [https://ieeexplore.ieee.org/abstract/document/8379359]

Tutorial

1. UFLDL Tutorial 1
   • [http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial]
2. UFLDL Tutorial 2

• [http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/]

3. Deep Learning for NLP (without Magic)

• [http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial]

4. A Deep Learning Tutorial: From Perceptrons to Deep Networks

• [http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks]

5. Deep Learning from the Bottom up

• [http://www.metacademy.org/roadmaps/rgrosse/deep_learning]

6. Theano Tutorial

• [http://deeplearning.net/tutorial/deeplearning.pdf]

7. Neural Networks for Matlab

• [http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf]

8. Using convolutional neural nets to detect facial keypoints tutorial

• [http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/]

9. Pytorch Tutorials

• [http://pytorch.org/tutorials/]

10. The Best Machine Learning Tutorials On The Web

• [https://github.com/josephmisiti/machine-learning-module]

11. VGG Convolutional Neural Networks Practical

• [http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html]

12. TensorFlow tutorials

• [https://github.com/nlintz/TensorFlow-Tutorials]

13. More TensorFlow tutorials

• [https://github.com/pkmital/tensorflow_tutorials]

13. TensorFlow Python Notebooks

• [https://github.com/aymericdamien/TensorFlow-Examples]

14. Keras and Lasagne Deep Learning Tutorials

• [https://github.com/Vict0rSch/deep_learning]

15. Classification on raw time series in TensorFlow with a LSTM RNN

• [https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition]

16. Using convolutional neural nets to detect facial keypoints tutorial

• [http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/]

17. TensorFlow-World

• [https://github.com/astorfi/TensorFlow-World]

18. Deep Learning NIPS’2015 Tutorial Geoff Hinton, Yoshua Bengio & Yann LeCun 深度学习三巨头共同主持

• [http://www.iro.umontreal.ca/~bengioy/talks/DL-Tutorial-NIPS2015.pdf]

视频教程

Courses

1. Machine Learning - Stanford

• [https://class.coursera.org/ml-005] by Andrew Ng in Coursera (2010-2014)

2. Machine Learning - Caltech

• [http://work.caltech.edu/lectures.html]by Yaser Abu-Mostafa (2012-2014)

3. Machine Learning - Carnegie Mellon

• [http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml]by Tom Mitchell (Spring 2011)

2. Neural Networks for Machine Learning

• [https://class.coursera.org/neuralnets-2012-001]by Geoffrey Hinton in Coursera (2012)

3. Neural networks class

• [https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH] by Hugo Larochelle from Université de Sherbrooke (2013)

4. Deep Learning Course

• [http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start] by CILVR lab @ NYU (2014)

5. A.I - Berkeley

• [https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/courseware/] by Dan Klein and Pieter Abbeel (2013)

6. A.I - MIT

• [http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/] by Patrick Henry Winston (2010)

7. Vision and learning - computers and brains

• [http://web.mit.edu/course/other/i2course/www/vision_and_learning_fall_2013.html]by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)

8. Convolutional Neural Networks for Visual Recognition - Stanford

• [http://vision.stanford.edu/teaching/cs231n/syllabus_winter2015.html] by Fei-Fei Li, Andrej Karpathy (2015)

9. Convolutional Neural Networks for Visual Recognition - Stanford

• [http://vision.stanford.edu/teaching/cs231n/syllabus.html] by Fei-Fei Li, Andrej Karpathy (2016)

10. Deep Learning for Natural Language Processing - Stanford

• [http://cs224d.stanford.edu/]

11. Neural Networks - usherbrooke

• [http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html]

12. Machine Learning - Oxford

• https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

13. Deep Learning - Nvidia

• [https://developer.nvidia.com/deep-learning-courses] (2015)

14. 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)

15. Deep Learning - Udacity/Google

• [https://www.udacity.com/course/deep-learning--ud730] by Vincent Vanhoucke and Arpan Chakraborty (2016)

16. Deep Learning - UWaterloo

• [https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE] by Prof. Ali Ghodsi at University of Waterloo (2015)

17. Statistical Machine Learning - CMU

• [https://www.youtube.com/watch?v=azaLcvuql_g&list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r]by Prof. Larry Wasserman

18. Deep Learning Course

• [https://www.college-de-france.fr/site/en-yann-lecun/course-2015-2016.htm] by Yann LeCun (2016)

19. Bay area DL school

• [http://www.bayareadlschool.org/] by Andrew Ng, Yoshua Bengio, Samy Bengio, Andrej Karpathy, Richard Socher, Hugo Larochelle and many others @ Stanford, CA (2016)

20. Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley

• [https://www.youtube.com/playlist?list=PLkFD6_40KJIxopmdJF_CLNqG3QuDFHQUm]

21. UVA Deep Learning Course

• [http://uvadlc.github.io] MSc in Artificial Intelligence for the University of Amsterdam.

22. MIT 6.S094: Deep Learning for Self-Driving Cars

• [http://selfdrivingcars.mit.edu/]

23. MIT 6.S191: Introduction to Deep Learning

• [http://introtodeeplearning.com/]

24. Berkeley CS 294: Deep Reinforcement Learning

• [http://rll.berkeley.edu/deeprlcourse/]

25. [Keras in Motion video course

• [https://www.manning.com/livevideo/keras-in-motion]

26. Practical Deep Learning For Coders

• [http://course.fast.ai/] by Jeremy Howard - Fast.ai

Videos and Lectures

1. How To Create A Mind

• [https://www.youtube.com/watch?v=RIkxVci-R4k] By Ray Kurzweil

2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning

• [https://www.youtube.com/watch?v=n1ViNeWhC24] By Andrew Ng

3. Recent Developments in Deep Learning

• [https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT] By Geoff Hinton

4. The Unreasonable Effectiveness of Deep Learning

• [https://www.youtube.com/watch?v=sc-KbuZqGkI] by Yann LeCun

5. Deep Learning of Representations

• [https://www.youtube.com/watch?v=4xsVFLnHC_0] by Yoshua bengio

6. Principles of Hierarchical Temporal Memory

• [https://www.youtube.com/watch?v=6ufPpZDmPKA] by Jeff Hawkins

7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab

• [https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT] by Adam Coates

8. Making Sense of the World with Deep Learning

• [http://vimeo.com/80821560] By Adam Coates

9. Demystifying Unsupervised Feature Learning

• [https://www.youtube.com/watch?v=wZfVBwOO0-k]By Adam Coates

10. Visual Perception with Deep Learning

• [https://www.youtube.com/watch?v=3boKlkPBckA]By Yann LeCun

11. The Next Generation of Neural Networks

• [https://www.youtube.com/watch?v=AyzOUbkUf3M] By Geoffrey Hinton at GoogleTechTalks

12. The wonderful and terrifying implications of computers that can learn

• [http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn] By Jeremy Howard at TEDxBrussels

13. Unsupervised Deep Learning - Stanford

• [http://web.stanford.edu/class/cs294a/handouts.html] by Andrew Ng in Stanford (2011)

14. Natural Language Processing

• [http://web.stanford.edu/class/cs224n/handouts/] By Chris Manning in Stanford

15. A beginners Guide to Deep Neural Networks

• [http://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html] By Natalie Hammel and Lorraine Yurshansky

16. Deep Learning: Intelligence from Big Data

• [https://www.youtube.com/watch?v=czLI3oLDe8M]by Steve Jurvetson (and panel) at VLAB in Stanford.

17. Introduction to Artificial Neural Networks and Deep Learning

• [https://www.youtube.com/watch?v=FoO8qDB8gUU] by Leo Isikdogan at Motorola Mobility HQ

18. NIPS 2016 lecture and workshop videos

• [https://nips.cc/Conferences/2016/Schedule]- NIPS 2016

代码

1. Caffe

• [http://caffe.berkeleyvision.org/]

2. Torch7

• [http://torch.ch/]

3. Theano

• [http://deeplearning.net/software/theano/]

4. cuda-convnet

• [https://code.google.com/p/cuda-convnet2/]

5. convetjs

• [https://github.com/karpathy/convnetjs]

6. Ccv

• [http://libccv.org/doc/doc-convnet/]

7. NuPIC -[http://numenta.org/nupic.html]
8. DeepLearning4J

• [http://deeplearning4j.org/]

9. Brain

• [https://github.com/harthur/brain]

10. DeepLearnToolbox

• [https://github.com/rasmusbergpalm/DeepLearnToolbox]

11. Deepnet

• [https://github.com/nitishsrivastava/deepnet]

12. Deeppy -[https://github.com/andersbll/deeppy]
13. JavaNN

• [https://github.com/ivan-vasilev/neuralnetworks]

14. hebel

• [https://github.com/hannes-brt/hebel]

15. Mocha.jl

• [https://github.com/pluskid/Mocha.jl]

16. OpenDL

• [https://github.com/guoding83128/OpenDL]

17. cuDNN

• [https://developer.nvidia.com/cuDNN]

18. MGL

• [http://melisgl.github.io/mgl-pax-world/mgl-manual.html]

19. Knet.jl

• [https://github.com/denizyuret/Knet.jl]

20. Nvidia DIGITS - a web app based on Caffe

• [https://github.com/NVIDIA/DIGITS]

21. Neon - Python based Deep Learning Framework

• [https://github.com/NervanaSystems/neon]

22. Keras - Theano based Deep Learning Library

• [http://keras.io]

23. Chainer - A flexible framework of neural networks for deep learning

• [http://chainer.org/]

24. RNNLM Toolkit

• [http://rnnlm.org/]

25. RNNLIB - A recurrent neural network library

• [http://sourceforge.net/p/rnnl/wiki/Home/]

26. char-rnn

• [https://github.com/karpathy/char-rnn]

27. MatConvNet: CNNs for MATLAB

• [https://github.com/vlfeat/matconvnet]

28. Minerva - a fast and flexible tool for deep learning on multi-GPU

• [https://github.com/dmlc/minerva]

29. Brainstorm - Fast, flexible and fun neural networks.

• [https://github.com/IDSIA/brainstorm]

30. Tensorflow - Open source software library for numerical computation using data flow graphs

• [https://github.com/tensorflow/tensorflow]

31. DMTK - Microsoft Distributed Machine Learning Tookit

• [https://github.com/Microsoft/DMTK]

32. Scikit Flow - Simplified interface for TensorFlow [mimicking Scikit Learn]

• [https://github.com/google/skflow]

33. MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework

• [https://github.com/dmlc/mxnet/]

34. Veles - Samsung Distributed machine learning platform

• [https://github.com/Samsung/veles]

35. Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework

• [https://github.com/PrincetonVision/marvin]

36. Apache SINGA - A General Distributed Deep Learning Platform

• [http://singa.incubator.apache.org/]

37. DSSTNE - Amazon's library for building Deep Learning models

• [https://github.com/amznlabs/amazon-dsstne]

38. SyntaxNet - Google's syntactic parser - A TensorFlow dependency library

• [https://github.com/tensorflow/models/tree/master/syntaxnet]

39. mlpack - A scalable Machine Learning library

• [http://mlpack.org/]

40. Torchnet - Torch based Deep Learning Library

• [https://github.com/torchnet/torchnet]

41. Paddle - PArallel Distributed Deep LEarning by Baidu

• [https://github.com/baidu/paddle]

42. NeuPy - Theano based Python library for ANN and Deep Learning

• [http://neupy.com]

43. Lasagne - a lightweight library to build and train neural networks in Theano

• [https://github.com/Lasagne/Lasagne]

44. nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne

• [https://github.com/dnouri/nolearn]

45. Sonnet - a library for constructing neural networks by Google's DeepMind

• [https://github.com/deepmind/sonnet]

46. PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

• [https://github.com/pytorch/pytorch]

47. CNTK - Microsoft Cognitive Toolkit

• [https://github.com/Microsoft/CNTK]

领域专家

1. Aaron Courville
   • [http://aaroncourville.wordpress.com]
2. Abdel-rahman Mohamed
   • [http://www.cs.toronto.edu/~asamir/]
3. Adam Coates
   • [http://cs.stanford.edu/~acoates/]
4. Alex Acero
   • [http://research.microsoft.com/en-us/people/alexac/]
5. Alex Krizhevsky
   • [http://www.cs.utoronto.ca/~kriz/index.html]
6. Alexander Ilin
   • [http://users.ics.aalto.fi/alexilin/]
7. Amos Storkey
   • [http://homepages.inf.ed.ac.uk/amos/]
8. Andrej Karpathy
   • [http://cs.stanford.edu/~karpathy/]
9. Andrew M. Saxe
   • [http://www.stanford.edu/~asaxe/]
10. Andrew Ng

• [http://www.cs.stanford.edu/people/ang/]

11. Andrew W. Senior

• [http://research.google.com/pubs/author37792.html]

12. Andriy Mnih

• [http://www.gatsby.ucl.ac.uk/~amnih/]

13. Ayse Naz Erkan

• [http://www.cs.nyu.edu/~naz/]

14. Benjamin Schrauwen

• [http://reslab.elis.ugent.be/benjamin]

15. Bernardete Ribeiro

• [https://www.cisuc.uc.pt/people/show/2020]

16. Bo David Chen

• [http://vision.caltech.edu/~bchen3/Site/Bo_David_Chen.html]

17. Boureau Y-Lan

• [http://cs.nyu.edu/~ylan/]

18. Brian Kingsbury

• [http://researcher.watson.ibm.com/researcher/view.php?person=us-bedk]

19. Christopher Manning

• [http://nlp.stanford.edu/~manning/]

20. Clement Farabet

• [http://www.clement.farabet.net/]

21. Dan Claudiu Cireșan

• [http://www.idsia.ch/~ciresan/]

22. David Reichert

• [http://serre-lab.clps.brown.edu/person/david-reichert/]

23. Derek Rose

• [http://mil.engr.utk.edu/nmil/member/5.html]

24. Dong Yu

• [http://research.microsoft.com/en-us/people/dongyu/default.aspx]

25. Drausin Wulsin

• [http://www.seas.upenn.edu/~wulsin/]

26. Erik M. Schmidt

• [http://music.ece.drexel.edu/people/eschmidt]

27. Eugenio Culurciello

• [https://engineering.purdue.edu/BME/People/viewPersonById?resource_id=71333]

28. Frank Seide

• [http://research.microsoft.com/en-us/people/fseide/]

29. Galen Andrew

• [http://homes.cs.washington.edu/~galen/]

30. Geoffrey Hinton

• [http://www.cs.toronto.edu/~hinton/]

31. George Dahl

• [http://www.cs.toronto.edu/~gdahl/]

32. Graham Taylor

• [http://www.uoguelph.ca/~gwtaylor/]

33. Grégoire Montavon

• [http://gregoire.montavon.name/]

34. Guido Francisco Montúfar

• [http://personal-homepages.mis.mpg.de/montufar/]

35. Guillaume Desjardins

• [http://brainlogging.wordpress.com/]

36. Hannes Schulz

• [http://www.ais.uni-bonn.de/~schulz/]

37. Hélène Paugam-Moisy

• [http://www.lri.fr/~hpaugam/]

38. Honglak Lee

• [http://web.eecs.umich.edu/~honglak/]

39. Hugo Larochelle

• [http://www.dmi.usherb.ca/~larocheh/index_en.html]

40. Ilya Sutskever

• [http://www.cs.toronto.edu/~ilya/]

41. Itamar Arel

• [http://mil.engr.utk.edu/nmil/member/2.html]

42. James Martens

• [http://www.cs.toronto.edu/~jmartens/]

43. Jason Morton

• [http://www.jasonmorton.com/]

44. Jason Weston

• [http://www.thespermwhale.com/jaseweston/]

45. Jeff Dean

• [http://research.google.com/pubs/jeff.html]

46. Jiquan Mgiam

• [http://cs.stanford.edu/~jngiam/]

47. Joseph Turian

• [http://www-etud.iro.umontreal.ca/~turian/]

48. Joshua Matthew Susskind

• [http://aclab.ca/users/josh/index.html]

49. Jürgen Schmidhuber

• [http://www.idsia.ch/~juergen/]

50. Justin A. Blanco

• [https://sites.google.com/site/blancousna/]

51. Koray Kavukcuoglu

• [http://koray.kavukcuoglu.org/]

52. KyungHyun Cho

• [http://users.ics.aalto.fi/kcho/]

53. Li Deng

• [http://research.microsoft.com/en-us/people/deng/]

54. Lucas Theis

• [http://www.kyb.tuebingen.mpg.de/nc/employee/details/lucas.html]

55. Ludovic Arnold

• [http://ludovicarnold.altervista.org/home/]

56. Marc'Aurelio Ranzato

• [http://www.cs.nyu.edu/~ranzato/]

57. Martin Längkvist

• [http://aass.oru.se/~mlt/]

58. Misha Denil

• [http://mdenil.com/]

59. Mohammad Norouzi

• [http://www.cs.toronto.edu/~norouzi/]

60. Nando de Freitas

• [http://www.cs.ubc.ca/~nando/]

61. Navdeep Jaitly

• [http://www.cs.utoronto.ca/~ndjaitly/]

62. Nicolas Le Roux

• [http://nicolas.le-roux.name/]

63. Nitish Srivastava

• [http://www.cs.toronto.edu/~nitish/]

64. Noel Lopes

• [https://www.cisuc.uc.pt/people/show/2028]

65. Oriol Vinyals

• [http://www.cs.berkeley.edu/~vinyals/]

66. Pascal Vincent

• [http://www.iro.umontreal.ca/~vincentp]

67. Patrick Nguyen

• [https://sites.google.com/site/drpngx/]

68. Pedro Domingos

• [http://homes.cs.washington.edu/~pedrod/]

69. Peggy Series

• [http://homepages.inf.ed.ac.uk/pseries/]

70. Pierre Sermanet

• [http://cs.nyu.edu/~sermanet]

71. Piotr Mirowski

• [http://www.cs.nyu.edu/~mirowski/]

72. Quoc V. Le

• [http://ai.stanford.edu/~quocle/]

73. Reinhold Scherer

• [http://bci.tugraz.at/scherer/]

74. Richard Socher

• [http://www.socher.org/]

75. Rob Fergus

• [http://cs.nyu.edu/~fergus/pmwiki/pmwiki.php]

76. Robert Coop

• [http://mil.engr.utk.edu/nmil/member/19.html]

77. Robert Gens

• [http://homes.cs.washington.edu/~rcg/]

78. Roger Grosse

• [http://people.csail.mit.edu/rgrosse/]

79. Ronan Collobert

• [http://ronan.collobert.com/]

80. Ruslan Salakhutdinov

• [http://www.utstat.toronto.edu/~rsalakhu/]

81. Sebastian Gerwinn

• [http://www.kyb.tuebingen.mpg.de/nc/employee/details/sgerwinn.html]

82. Stéphane Mallat

• [http://www.cmap.polytechnique.fr/~mallat/]

83. Sven Behnke

• [http://www.ais.uni-bonn.de/behnke/]

84. Tapani Raiko

• [http://users.ics.aalto.fi/praiko/]

85. Tara Sainath

• [https://sites.google.com/site/tsainath/]

86. Tijmen Tieleman

• [http://www.cs.toronto.edu/~tijmen/]

87. Tom Karnowski

• [http://mil.engr.utk.edu/nmil/member/36.html]

88. Tomáš Mikolov

• [https://research.facebook.com/tomas-mikolov]

89. Ueli Meier

• [http://www.idsia.ch/~meier/]

90. Vincent Vanhoucke

• [http://vincent.vanhoucke.com]

91. Volodymyr Mnih

• [http://www.cs.toronto.edu/~vmnih/]

92. Yann LeCun

• [http://yann.lecun.com/]

93. Yichuan Tang

• [http://www.cs.toronto.edu/~tang/]

94. Yoshua Bengio

• [http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html]

95. Yotaro Kubo

• [http://yota.ro/]

96. Youzhi [Will] Zou

• [http://ai.stanford.edu/~wzou]

97. Fei-Fei Li

• [http://vision.stanford.edu/feifeili]

98. Ian Goodfellow

• [https://research.google.com/pubs/105214.html]

99. Robert Laganière

• [http://www.site.uottawa.ca/~laganier/]

重要网站收藏

1. deeplearning.net
   • [http://deeplearning.net/]
2. deeplearning.stanford.edu
   • [http://deeplearning.stanford.edu/]
3. nlp.stanford.edu
   • [http://nlp.stanford.edu/]
4. ai-junkie.com
   • [http://www.ai-junkie.com/ann/evolved/nnt1.html]
5. cs.brown.edu/research/ai
   • [http://cs.brown.edu/research/ai/]
6. eecs.umich.edu/ai
   • [http://www.eecs.umich.edu/ai/]
7. cs.utexas.edu/users/ai-lab
   • [http://www.cs.utexas.edu/users/ai-lab/]
8. cs.washington.edu/research/ai
   • [http://www.cs.washington.edu/research/ai/]
9. aiai.ed.ac.uk
   • [http://www.aiai.ed.ac.uk/]
10. www-aig.jpl.nasa.gov

• [http://www-aig.jpl.nasa.gov/]

11. csail.mit.edu

• [http://www.csail.mit.edu/]

12. cgi.cse.unsw.edu.au/~aishare

• [http://cgi.cse.unsw.edu.au/~aishare/]

13. cs.rochester.edu/research/ai

• [http://www.cs.rochester.edu/research/ai/]

14. ai.sri.com

• [http://www.ai.sri.com/]

15. isi.edu/AI/isd.htm

• [http://www.isi.edu/AI/isd.htm]

16. nrl.navy.mil/itd/aic

• [http://www.nrl.navy.mil/itd/aic/]

17. hips.seas.harvard.edu

• [http://hips.seas.harvard.edu/]

18. AI Weekly

• [http://aiweekly.co]

19. stat.ucla.edu

• [http://www.stat.ucla.edu/~junhua.mao/m-RNN.html]

20. deeplearning.cs.toronto.edu

• [http://deeplearning.cs.toronto.edu/i2t]

21. jeffdonahue.com/lrcn/

• [http://jeffdonahue.com/lrcn/]

22. visualqa.org

• [http://www.visualqa.org/]

23. www.mpi-inf.mpg.de/departments/computer-vision...

• [https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/]

24. Deep Learning News

• [http://news.startup.ml/]

25. Machine Learning is Fun! Adam Geitgey's Blog

• [https://medium.com/@ageitgey/]

免费在线图书

1. Deep Learning
   • [http://www.iro.umontreal.ca/~bengioy/dlbook/] by Yoshua Bengio, Ian Goodfellow and Aaron Courville [05/07/2015]
   • 中文版：[https://github.com/exacity/deeplearningbook-chinese]
2. Neural Networks and Deep Learning
   • [http://neuralnetworksanddeeplearning.com/] by Michael Nielsen [Dec 2014]
3. Deep Learning
   • [http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf] by Microsoft Research [2013]
4. Deep Learning Tutorial
   • [http://deeplearning.net/tutorial/deeplearning.pdf] by LISA lab, University of Montreal [Jan 6 2015]
5. neuraltalk
   • [https://github.com/karpathy/neuraltalk] by Andrej Karpathy : numpy-based RNN/LSTM implementation
6. An introduction to genetic algorithms
   • [https://svn-d1.mpi-inf.mpg.de/AG1/MultiCoreLab/papers/ebook-fuzzy-mitchell-99.pdf]
7. Artificial Intelligence: A Modern Approach
   • [http://aima.cs.berkeley.edu/]
8. Deep Learning in Neural Networks: An Overview
   • [http://arxiv.org/pdf/1404.7828v4.pdf]
9. 神经网络与深度学习
   • [https://nndl.github.io/]
   • [https://nndl.github.io/nndl-book.pdf],2021.5
10. 动手深度学习Release 2.0.0-beta0
    • [https://zh.d2l.ai/index.html]
    • [https://zh.d2l.ai/d2l-zh.pdf],2022.17

Datasets

1. MNIST
   • [http://yann.lecun.com/exdb/mnist/] Handwritten digits
2. Google House Numbers
   • [http://ufldl.stanford.edu/housenumbers/] from street view
3. CIFAR-10 and CIFAR-100
   • [http://www.cs.toronto.edu/~kriz/cifar.html]
4. IMAGENET
   • [http://www.image-net.org/]
5. Tiny Images
   • [http://groups.csail.mit.edu/vision/TinyImages/] 80 Million tiny images6.
6. Flickr Data
   • [https://yahooresearch.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images] 100 Million Yahoo dataset
7. Berkeley Segmentation Dataset 500
   • [http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/]
8. UC Irvine Machine Learning Repository
   • [http://archive.ics.uci.edu/ml/]
9. Flickr 8k
   • [http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html]
10. Flickr 30k
    • [http://shannon.cs.illinois.edu/DenotationGraph/]
11. Microsoft COCO
    • [http://mscoco.org/home/]
12. VQA
    • [http://www.visualqa.org/]
13. Image QA
    • [http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/]
14. AT&T Laboratories Cambridge face database
    • [http://www.uk.research.att.com/facedatabase.html]
15. AVHRR Pathfinder
    • [http://xtreme.gsfc.nasa.gov]
16. Air Freight
    • [http://www.anc.ed.ac.uk/~amos/afreightdata.html] - 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
    • [http://www.science.uva.nl/~aloi/] - 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
    • [http://www.imm.dtu.dk/~aam/] - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. [Formats: bmp,asf]
19. Image Analysis and Computer Graphics
    • [http://www.imm.dtu.dk/image/]
20. Brown University Stimuli
    • [http://www.cog.brown.edu/~tarr/stimuli.html] - 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
    • [http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/] - 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
    • [http://www.ipab.inf.ed.ac.uk/mvu/]
23. CCITT Fax standard images
    • [http://www.cs.waikato.ac.nz/~singlis/ccitt.html] - 8 images [Formats: gif]
24. CMU CIL's Stereo Data with Ground Truth[cil-ster.html] - 3 sets of 11 images, including color tiff images with spectroradiometry [Formats: gif, tiff]
25. CMU PIE Database
    • [http://www.ri.cmu.edu/projects/project_418.html] - 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
    • [http://www.ius.cs.cmu.edu/idb/] - Images, sequences, stereo pairs [thousands of images] [Formats: Sun Rasterimage]
27. Caltech Image Database
    • [http://www.vision.caltech.edu/html-files/archive.html] - about 20 images - mostly top-down views of small objects and toys. [Formats: GIF]
28. Columbia-Utrecht Reflectance and Texture Database
    • [http://www.cs.columbia.edu/CAVE/curet/] - 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
    • [http://www.cs.sfu.ca/~colour/data/index.html] - 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
    • [http://www.cs.sfu.ca/~colour/]
31. Content-based image retrieval database
    • [http://www.cs.washington.edu/research/imagedatabase/groundtruth/] - 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
    • [http://www.cs.washington.edu/research/imagedatabase/]
33. Densely Sampled View Spheres
    • [http://ls7-www.cs.uni-dortmund.de/~peters/pages/research/modeladaptsys/modeladaptsys_vba_rov.html] - 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]
    • [http://ls7-www.cs.uni-dortmund.de/]
35. Digital Embryos
    • [https://web-beta.archive.org/web/20011216051535/vision.psych.umn.edu/www/kersten-lab/demos/digitalembryo.html] - 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
    • [http://vision.psych.umn.edu/www/kersten-lab/kersten-lab.html]
37. El Salvador Atlas of Gastrointestinal VideoEndoscopy
    • [http://www.gastrointestinalatlas.com] - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. [Formats: jpg, mpg, gif]
38. FG-NET Facial Aging Database
    • [http://sting.cycollege.ac.cy/~alanitis/fgnetaging/index.htm] - Database contains 1002 face images showing subjects at different ages. [Formats: jpg]
39. FVC2000 Fingerprint Databases
    • [http://bias.csr.unibo.it/fvc2000/] - 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
    • [http://bias.csr.unibo.it/research/biolab] - University of Bologna
41. Face and Gesture images and image sequences
    • [http://www.fg-net.org] - Several image datasets of faces and gestures that are ground truth annotated for benchmarking
42. German Fingerspelling Database
    • [http://www-i6.informatik.rwth-aachen.de/~dreuw/database.html] - 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
    • [http://www-i6.informatik.rwth-aachen.de/]
44. Groningen Natural Image Database
    • [http://hlab.phys.rug.nl/archive.html] - 4000+ 1536x1024 [16 bit] calibrated outdoor images [Formats: homebrew]
45. ICG Testhouse sequence
    • [http://www.icg.tu-graz.ac.at/~schindler/Data] - 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color [Formats: PPM]
46. Institute of Computer Graphics and Vision
    • [http://www.icg.tu-graz.ac.at]
47. IEN Image Library
    • [http://www.ien.it/is/vislib/] - 1000+ images, mostly outdoor sequences [Formats: raw, ppm]
48. INRIA's Syntim images database
    • [http://www-rocq.inria.fr/~tarel/syntim/images.html] - 15 color image of simple objects [Formats: gif]
49. INRIA
    • [http://www.inria.fr/]
50. INRIA's Syntim stereo databases
    • [http://www-rocq.inria.fr/~tarel/syntim/paires.html] - 34 calibrated color stereo pairs [Formats: gif]
51. Image Analysis Laboratory
    • [http://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/index.html] - 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
    • [http://www.ece.ncsu.edu/imaging]
53. Image Database
    • [http://www.prip.tuwien.ac.at/prip/image.html] - An image database including some textures
54. JAFFE Facial Expression Image Database
    • [http://www.mis.atr.co.jp/~mlyons/jaffe.html] - 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
    • [http://www.mic.atr.co.jp/]
56. JISCT Stereo Evaluation
    • [ftp://ftp.vislist.com/IMAGERY/JISCT/] - 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
    • [http://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html] - Image archive [100+ images] [Formats: ppm]
58. MIT face images and more
    • [ftp://whitechapel.media.mit.edu/pub/images] - hundreds of images [Formats: homebrew]
59. Machine Vision
    • [http://vision.cse.psu.edu/book/testbed/images/] - Images from the textbook by Jain, Kasturi, Schunck [20+ images] [Formats: GIF TIFF]
60. Mammography Image Databases
    • [http://marathon.csee.usf.edu/Mammography/Database.html] - 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
    • [ftp://ftp.cps.msu.edu/pub/prip] - many images [Formats: unknown]
62. Middlebury Stereo Data Sets with Ground Truth
    • [http://www.middlebury.edu/stereo/data.html] - 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
    • [http://www.middlebury.edu/stereo] - Middlebury College
64. Modis Airborne simulator, Gallery and data set
    • [http://ltpwww.gsfc.nasa.gov/MODIS/MAS/] - 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
    • [ftp://sequoyah.ncsl.nist.gov/pub/databases/data] - datasets - thousands of images [Formats: unknown]
66. NIST Fingerprint data
    • [ftp://ftp.cs.columbia.edu/jpeg/other/uuencoded] - compressed multipart uuencoded tar file
67. NLM HyperDoc Visible Human Project
    • [http://www.nlm.nih.gov/research/visible/visible_human.html] - Color, CAT and MRI image samples - over 30 images [Formats: jpeg]
68. National Design Repository
    • [http://www.designrepository.org] - 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
    • [http://gicl.mcs.drexel.edu]
70. OSU [MSU] 3D Object Model Database
    • [http://eewww.eng.ohio-state.edu/~flynn/3DDB/Models/] - 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
    • [http://eewww.eng.ohio-state.edu/~flynn/3DDB/RID/] - Hundreds of real and synthetic images [Formats: gif, homebrew]
72. OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences
    • [http://sampl.eng.ohio-state.edu/~sampl/database.htm] - Over 1000 range images, 3D object models, still images and motion sequences [Formats: gif, ppm, vrml, homebrew]
73. Signal Analysis and Machine Perception Laboratory
    • [http://sampl.eng.ohio-state.edu]
74. Otago Optical Flow Evaluation Sequences
    • [http://www.cs.otago.ac.nz/research/vision/Research/OpticalFlow/opticalflow.html] - 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
    • [http://www.cs.otago.ac.nz/research/vision/index.html]
76. ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/
    • [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
    • [http://www.limsi.fr/Recherche/IMM/PageIMM.html]
78. LIMSI-CNRS
    • [http://www.limsi.fr/]
79. Photometric 3D Surface Texture Database
    • [http://www.taurusstudio.net/research/pmtexdb/index.htm] - 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]
    • [http://www.cee.hw.ac.uk/~mtc/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
    • [http://www.cee.hw.ac.uk/~mtc/research.html]
82. Sequences for Flow Based Reconstruction
    • [http://www.nada.kth.se/~zucch/CAMERA/PUB/seq.html] - synthetic sequence for testing structure from motion algorithms [Formats: pgm]
83. Stereo Images with Ground Truth Disparity and Occlusion
    • [http://www-dbv.cs.uni-bonn.de/stereo_data/] - 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
    • [http://range.informatik.uni-stuttgart.de] - A collection of synthetic range images taken from high-resolution polygonal models available on the web [Formats: homebrew]
85. Department Image Understanding
    • [http://www.informatik.uni-stuttgart.de/ipvr/bv/bv_home_engl.html]
86. The AR Face Database
    • [http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html] - 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

• [http://rvl.www.ecn.purdue.edu/RVL/]

101. The MIT-CSAIL Database of Objects and Scenes

• [http://web.mit.edu/torralba/www/database.html] - 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]

102. The RVL SPEC-DB [SPECularity DataBase]

• [http://rvl1.ecn.purdue.edu/RVL/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 ]

103. Robot Vision Laboratory

• [http://rvl1.ecn.purdue.edu/RVL/]

105. The Xm2vts database

• [http://xm2vtsdb.ee.surrey.ac.uk] - 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.

106. Centre for Vision, Speech and Signal Processing

• [http://www.ee.surrey.ac.uk/Research/CVSSP]

107. Traffic Image Sequences and 'Marbled Block' Sequence

• [http://i21www.ira.uka.de/image_sequences] - thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' sequence [grayscale images] [Formats: GIF]

108. IAKS/KOGS

• [http://i21www.ira.uka.de]

110. U Bern Face images

• [ftp://ftp.iam.unibe.ch/pub/Images/FaceImages] - hundreds of images [Formats: Sun rasterfile]

111. U Michigan textures

• [ftp://freebie.engin.umich.edu/pub/misc/textures] [Formats: compressed raw]

112. U Oulu wood and knots database

• [http://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html] - Includes classifications - 1000+ color images [Formats: ppm]

113. UCID - an Uncompressed Colour Image Database

• [http://vision.doc.ntu.ac.uk/datasets/UCID/ucid.html] - a benchmark database for image retrieval with predefined ground truth. [Formats: tiff]

115. UMass Vision Image Archive

• [http://vis-www.cs.umass.edu/~vislib/] - Large image database with aerial, space, stereo, medical images and more. [Formats: homebrew]

116. UNC's 3D image database

• [ftp://sunsite.unc.edu/pub/academic/computer-science/virtual-reality/3d] - many images [Formats: GIF]

117. USF Range Image Data with Segmentation Ground Truth

• [http://marathon.csee.usf.edu/range/seg-comp/SegComp.html] - 80 image sets [Formats: Sun rasterimage]

118. University of Oulu Physics-based Face Database

• [http://www.ee.oulu.fi/research/imag/color/pbfd.html] - contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral reflectance measurements of each person.

119. Machine Vision and Media Processing Unit

• [http://www.ee.oulu.fi/mvmp/]

121. University of Oulu Texture Database

• [http://www.outex.oulu.fi] - 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]

122. Machine Vision Group

• [http://www.ee.oulu.fi/mvg]

124. Usenix face database

• [ftp://ftp.uu.net/published/usenix/faces] - Thousands of face images from many different sites [circa 994]

125. View Sphere Database

• [http://www-prima.inrialpes.fr/Prima/hall/view_sphere.html] - 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]

126. PRIMA, GRAVIR

• [http://www-prima.inrialpes.fr/Prima/]

127. Vision-list Imagery Archive

• [ftp://ftp.vislist.com/IMAGERY/] - Many images, many formats

128. Wiry Object Recognition Database

• [http://www.cs.cmu.edu/~owenc/word.htm] - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. [Formats: jpg]

129. 3D Vision Group

• [http://www.cs.cmu.edu/0.000000E+003dvision/]

131. Yale Face Database

• [http://cvc.yale.edu/projects/yalefaces/yalefaces.html] - 165 images [15 individuals] with different lighting, expression, and occlusion configurations.

132. Yale Face Database B

• [http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html] - 5760 single light source images of 10 subjects each seen under 576 viewing conditions [9 poses x 64 illumination conditions]. [Formats: PGM]

133. Center for Computational Vision and Control
     • [http://cvc.yale.edu/]
134. DeepMind QA Corpus
     • [https://github.com/deepmind/rc-data] - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. Paper[http://arxiv.org/abs/1506.03340] for reference.
135. YouTube-8M Dataset
     • [https://research.google.com/youtube8m/] - YouTube-8M is a large-scale labeled video dataset that consists of 8 million YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities.
136. Open Images dataset
     • [https://github.com/openimages/dataset] - Open Images is a dataset of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories.

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最近更新：2022-2-16