floodsung / deep-learning-papers-reading-roadmap Goto Github PK

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

Python 100.00%

deep-learning-papers-reading-roadmap's Introduction

Deep Learning Papers Reading Roadmap

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?"

Here is a reading roadmap of Deep Learning papers!

The roadmap is constructed in accordance with the following four guidelines:

From outline to detail
From old to state-of-the-art
from generic to specific areas
focus on state-of-the-art

You will find many papers that are quite new but really worth reading.

I would continue adding papers to this roadmap.

1 Deep Learning History and Basics

1.0 Book

[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book. (2015). [html] (Deep Learning Bible, you can read this book while reading following papers.) ⭐⭐⭐⭐⭐

1.1 Survey

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

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

[2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554. [pdf](Deep Learning Eve) ⭐⭐⭐

[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507. [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐

1.3 ImageNet Evolution（Deep Learning broke out from here）

[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. [pdf] (AlexNet, Deep Learning Breakthrough) ⭐⭐⭐⭐⭐

[5] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). [pdf] (VGGNet,Neural Networks become very deep!) ⭐⭐⭐

[6] Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [pdf] (GoogLeNet) ⭐⭐⭐

[7] He, Kaiming, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1512.03385 (2015). [pdf] (ResNet,Very very deep networks, CVPR best paper) ⭐⭐⭐⭐⭐

1.4 Speech Recognition Evolution

[8] Hinton, Geoffrey, et al. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." IEEE Signal Processing Magazine 29.6 (2012): 82-97. [pdf] (Breakthrough in speech recognition)⭐⭐⭐⭐

[9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. "Speech recognition with deep recurrent neural networks." 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (RNN)⭐⭐⭐

[10] Graves, Alex, and Navdeep Jaitly. "Towards End-To-End Speech Recognition with Recurrent Neural Networks." ICML. Vol. 14. 2014. [pdf]⭐⭐⭐

[11] Sak, Haşim, et al. "Fast and accurate recurrent neural network acoustic models for speech recognition." arXiv preprint arXiv:1507.06947 (2015). [pdf] (Google Speech Recognition System) ⭐⭐⭐

[12] Amodei, Dario, et al. "Deep speech 2: End-to-end speech recognition in english and mandarin." arXiv preprint arXiv:1512.02595 (2015). [pdf] (Baidu Speech Recognition System) ⭐⭐⭐⭐

[13] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig "Achieving Human Parity in Conversational Speech Recognition." arXiv preprint arXiv:1610.05256 (2016). [pdf] (State-of-the-art in speech recognition, Microsoft) ⭐⭐⭐⭐

After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers. I suggest that you can choose the following papers based on your interests and research direction.

#2 Deep Learning Method

2.1 Model

[14] Hinton, Geoffrey E., et al. "Improving neural networks by preventing co-adaptation of feature detectors." arXiv preprint arXiv:1207.0580 (2012). [pdf] (Dropout) ⭐⭐⭐

[15] Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958. [pdf] ⭐⭐⭐

[16] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015). [pdf] (An outstanding Work in 2015) ⭐⭐⭐⭐

[17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. "Layer normalization." arXiv preprint arXiv:1607.06450 (2016). [pdf] (Update of Batch Normalization) ⭐⭐⭐⭐

[18] Courbariaux, Matthieu, et al. "Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1." [pdf] (New Model,Fast) ⭐⭐⭐

[19] Jaderberg, Max, et al. "Decoupled neural interfaces using synthetic gradients." arXiv preprint arXiv:1608.05343 (2016). [pdf] (Innovation of Training Method,Amazing Work) ⭐⭐⭐⭐⭐

[20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. "Net2net: Accelerating learning via knowledge transfer." arXiv preprint arXiv:1511.05641 (2015). [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐

[21] Wei, Tao, et al. "Network Morphism." arXiv preprint arXiv:1603.01670 (2016). [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐

2.2 Optimization

[22] Sutskever, Ilya, et al. "On the importance of initialization and momentum in deep learning." ICML (3) 28 (2013): 1139-1147. [pdf] (Momentum optimizer) ⭐⭐

[23] Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014). [pdf] (Maybe used most often currently) ⭐⭐⭐

[24] Andrychowicz, Marcin, et al. "Learning to learn by gradient descent by gradient descent." arXiv preprint arXiv:1606.04474 (2016). [pdf] (Neural Optimizer,Amazing Work) ⭐⭐⭐⭐⭐

[25] 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). [pdf] (ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup) ⭐⭐⭐⭐⭐

[26] Iandola, Forrest N., et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." arXiv preprint arXiv:1602.07360 (2016). [pdf] (Also a new direction to optimize NN,DeePhi Tech Startup) ⭐⭐⭐⭐

[27] Glorat Xavier, Bengio Yoshua, et al. "Understanding the difficulty of training deep forward neural networks." Proceedings of the thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249-256,2010. [pdf] ⭐⭐⭐⭐

2.3 Unsupervised Learning / Deep Generative Model

[28] Le, Quoc V. "Building high-level features using large scale unsupervised learning." 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat) ⭐⭐⭐⭐

[29] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). [pdf] (VAE) ⭐⭐⭐⭐

[30] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in Neural Information Processing Systems. 2014. [pdf] (GAN,super cool idea) ⭐⭐⭐⭐⭐

[31] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). [pdf] (DCGAN) ⭐⭐⭐⭐

[32] Gregor, Karol, et al. "DRAW: A recurrent neural network for image generation." arXiv preprint arXiv:1502.04623 (2015). [pdf] (VAE with attention, outstanding work) ⭐⭐⭐⭐⭐

[33] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. "Pixel recurrent neural networks." arXiv preprint arXiv:1601.06759 (2016). [pdf] (PixelRNN) ⭐⭐⭐⭐

[34] Oord, Aaron van den, et al. "Conditional image generation with PixelCNN decoders." arXiv preprint arXiv:1606.05328 (2016). [pdf] (PixelCNN) ⭐⭐⭐⭐

[34] S. Mehri et al., "SampleRNN: An Unconditional End-to-End Neural Audio Generation Model." arXiv preprint arXiv:1612.07837 (2016). [pdf] ⭐⭐⭐⭐⭐

2.4 RNN / Sequence-to-Sequence Model

[35] Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013). [pdf] (LSTM, very nice generating result, show the power of RNN) ⭐⭐⭐⭐

[36] Cho, Kyunghyun, et al. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014). [pdf] (First Seq-to-Seq Paper) ⭐⭐⭐⭐

[37] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural information processing systems. 2014. [pdf] (Outstanding Work) ⭐⭐⭐⭐⭐

[38] Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. "Neural Machine Translation by Jointly Learning to Align and Translate." arXiv preprint arXiv:1409.0473 (2014). [pdf] ⭐⭐⭐⭐

[39] Vinyals, Oriol, and Quoc Le. "A neural conversational model." arXiv preprint arXiv:1506.05869 (2015). [pdf] (Seq-to-Seq on Chatbot) ⭐⭐⭐

2.5 Neural Turing Machine

[40] Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural turing machines." arXiv preprint arXiv:1410.5401 (2014). [pdf] (Basic Prototype of Future Computer) ⭐⭐⭐⭐⭐

[41] Zaremba, Wojciech, and Ilya Sutskever. "Reinforcement learning neural Turing machines." arXiv preprint arXiv:1505.00521 362 (2015). [pdf] ⭐⭐⭐

[42] Weston, Jason, Sumit Chopra, and Antoine Bordes. "Memory networks." arXiv preprint arXiv:1410.3916 (2014). [pdf] ⭐⭐⭐

[43] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." Advances in neural information processing systems. 2015. [pdf] ⭐⭐⭐⭐

[44] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." Advances in Neural Information Processing Systems. 2015. [pdf] ⭐⭐⭐⭐

[45] Graves, Alex, et al. "Hybrid computing using a neural network with dynamic external memory." Nature (2016). [pdf] (Milestone,combine above papers' ideas) ⭐⭐⭐⭐⭐

2.6 Deep Reinforcement Learning

[46] Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013). [pdf]) (First Paper named deep reinforcement learning) ⭐⭐⭐⭐

[47] Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533. [pdf] (Milestone) ⭐⭐⭐⭐⭐

[48] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. "Dueling network architectures for deep reinforcement learning." arXiv preprint arXiv:1511.06581 (2015). [pdf] (ICLR best paper,great idea) ⭐⭐⭐⭐

[49] Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning." arXiv preprint arXiv:1602.01783 (2016). [pdf] (State-of-the-art method) ⭐⭐⭐⭐⭐

[50] Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015). [pdf] (DDPG) ⭐⭐⭐⭐

[51] Gu, Shixiang, et al. "Continuous Deep Q-Learning with Model-based Acceleration." arXiv preprint arXiv:1603.00748 (2016). [pdf] (NAF) ⭐⭐⭐⭐

[52] Schulman, John, et al. "Trust region policy optimization." CoRR, abs/1502.05477 (2015). [pdf] (TRPO) ⭐⭐⭐⭐

[53] Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489. [pdf] (AlphaGo) ⭐⭐⭐⭐⭐

2.7 Deep Transfer Learning / Lifelong Learning / especially for RL

[54] Bengio, Yoshua. "Deep Learning of Representations for Unsupervised and Transfer Learning." ICML Unsupervised and Transfer Learning 27 (2012): 17-36. [pdf] (A Tutorial) ⭐⭐⭐

[55] Silver, Daniel L., Qiang Yang, and Lianghao Li. "Lifelong Machine Learning Systems: Beyond Learning Algorithms." AAAI Spring Symposium: Lifelong Machine Learning. 2013. [pdf] (A brief discussion about lifelong learning) ⭐⭐⭐

[56] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015). [pdf] (Godfather's Work) ⭐⭐⭐⭐

[57] Rusu, Andrei A., et al. "Policy distillation." arXiv preprint arXiv:1511.06295 (2015). [pdf] (RL domain) ⭐⭐⭐

[58] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. "Actor-mimic: Deep multitask and transfer reinforcement learning." arXiv preprint arXiv:1511.06342 (2015). [pdf] (RL domain) ⭐⭐⭐

[59] Rusu, Andrei A., et al. "Progressive neural networks." arXiv preprint arXiv:1606.04671 (2016). [pdf] (Outstanding Work, A novel idea) ⭐⭐⭐⭐⭐

2.8 One Shot Deep Learning

[60] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. "Human-level concept learning through probabilistic program induction." Science 350.6266 (2015): 1332-1338. [pdf] (No Deep Learning,but worth reading) ⭐⭐⭐⭐⭐

[61] Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. "Siamese Neural Networks for One-shot Image Recognition."(2015) [pdf] ⭐⭐⭐

[62] Santoro, Adam, et al. "One-shot Learning with Memory-Augmented Neural Networks." arXiv preprint arXiv:1605.06065 (2016). [pdf] (A basic step to one shot learning) ⭐⭐⭐⭐

[63] Vinyals, Oriol, et al. "Matching Networks for One Shot Learning." arXiv preprint arXiv:1606.04080 (2016). [pdf] ⭐⭐⭐

[64] Hariharan, Bharath, and Ross Girshick. "Low-shot visual object recognition." arXiv preprint arXiv:1606.02819 (2016). [pdf] (A step to large data) ⭐⭐⭐⭐

3 Applications

3.1 NLP(Natural Language Processing)

[1] Antoine Bordes, et al. "Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing." AISTATS(2012) [pdf] ⭐⭐⭐⭐

[2] Mikolov, et al. "Distributed representations of words and phrases and their compositionality." ANIPS(2013): 3111-3119 [pdf] (word2vec) ⭐⭐⭐

[3] Sutskever, et al. "“Sequence to sequence learning with neural networks." ANIPS(2014) [pdf] ⭐⭐⭐

[4] Ankit Kumar, et al. "“Ask Me Anything: Dynamic Memory Networks for Natural Language Processing." arXiv preprint arXiv:1506.07285(2015) [pdf] ⭐⭐⭐⭐

[5] Yoon Kim, et al. "Character-Aware Neural Language Models." NIPS(2015) arXiv preprint arXiv:1508.06615(2015) [pdf] ⭐⭐⭐⭐

[6] Jason Weston, et al. "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks." arXiv preprint arXiv:1502.05698(2015) [pdf] (bAbI tasks) ⭐⭐⭐

[7] Karl Moritz Hermann, et al. "Teaching Machines to Read and Comprehend." arXiv preprint arXiv:1506.03340(2015) [pdf] (CNN/DailyMail cloze style questions) ⭐⭐

[8] Alexis Conneau, et al. "Very Deep Convolutional Networks for Natural Language Processing." arXiv preprint arXiv:1606.01781(2016) [pdf] (state-of-the-art in text classification) ⭐⭐⭐

[9] Armand Joulin, et al. "Bag of Tricks for Efficient Text Classification." arXiv preprint arXiv:1607.01759(2016) [pdf] (slightly worse than state-of-the-art, but a lot faster) ⭐⭐⭐

3.2 Object Detection

[1] Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. "Deep neural networks for object detection." Advances in Neural Information Processing Systems. 2013. [pdf] ⭐⭐⭐

[2] 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. [pdf] (RCNN) ⭐⭐⭐⭐⭐

[3] He, Kaiming, et al. "Spatial pyramid pooling in deep convolutional networks for visual recognition." European Conference on Computer Vision. Springer International Publishing, 2014. [pdf] (SPPNet) ⭐⭐⭐⭐

[4] Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] ⭐⭐⭐⭐

[5] Ren, Shaoqing, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015. [pdf] ⭐⭐⭐⭐

[6] Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015). [pdf] (YOLO,Oustanding Work, really practical) ⭐⭐⭐⭐⭐

[7] Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." arXiv preprint arXiv:1512.02325 (2015). [pdf] ⭐⭐⭐

[8] Dai, Jifeng, et al. "R-FCN: Object Detection via Region-based Fully Convolutional Networks." arXiv preprint arXiv:1605.06409 (2016). [pdf] ⭐⭐⭐⭐

[9] He, Gkioxari, et al. "Mask R-CNN" arXiv preprint arXiv:1703.06870 (2017). [pdf] ⭐⭐⭐⭐

[10] Bochkovskiy, Alexey, et al. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020). [pdf] ⭐⭐⭐⭐

[11] Tan, Mingxing, et al. “EfficientDet: Scalable and Efficient Object Detection." arXiv preprint arXiv:1911.09070 (2019). [pdf] ⭐⭐⭐⭐⭐

3.3 Visual Tracking

[1] Wang, Naiyan, and Dit-Yan Yeung. "Learning a deep compact image representation for visual tracking." Advances in neural information processing systems. 2013. [pdf] (First Paper to do visual tracking using Deep Learning,DLT Tracker) ⭐⭐⭐

[2] Wang, Naiyan, et al. "Transferring rich feature hierarchies for robust visual tracking." arXiv preprint arXiv:1501.04587 (2015). [pdf] (SO-DLT) ⭐⭐⭐⭐

[3] Wang, Lijun, et al. "Visual tracking with fully convolutional networks." Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] (FCNT) ⭐⭐⭐⭐

[4] Held, David, Sebastian Thrun, and Silvio Savarese. "Learning to Track at 100 FPS with Deep Regression Networks." arXiv preprint arXiv:1604.01802 (2016). [pdf] (GOTURN,Really fast as a deep learning method,but still far behind un-deep-learning methods) ⭐⭐⭐⭐

[5] Bertinetto, Luca, et al. "Fully-Convolutional Siamese Networks for Object Tracking." arXiv preprint arXiv:1606.09549 (2016). [pdf] (SiameseFC,New state-of-the-art for real-time object tracking) ⭐⭐⭐⭐

[6] Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. "Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." ECCV (2016) [pdf] (C-COT) ⭐⭐⭐⭐

[7] Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." arXiv preprint arXiv:1608.07242 (2016). [pdf] (VOT2016 Winner,TCNN) ⭐⭐⭐⭐

3.4 Image Caption

[1] Farhadi,Ali,etal. "Every picture tells a story: Generating sentences from images". In Computer VisionECCV 2010. Springer Berlin Heidelberg:15-29, 2010. [pdf] ⭐⭐⭐

[2] Kulkarni, Girish, et al. "Baby talk: Understanding and generating image descriptions". In Proceedings of the 24th CVPR, 2011. [pdf]⭐⭐⭐⭐

[3] Vinyals, Oriol, et al. "Show and tell: A neural image caption generator". In arXiv preprint arXiv:1411.4555, 2014. [pdf]⭐⭐⭐

[4] Donahue, Jeff, et al. "Long-term recurrent convolutional networks for visual recognition and description". In arXiv preprint arXiv:1411.4389 ,2014. [pdf]

[5] Karpathy, Andrej, and Li Fei-Fei. "Deep visual-semantic alignments for generating image descriptions". In arXiv preprint arXiv:1412.2306, 2014. [pdf]⭐⭐⭐⭐⭐

[6] 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. [pdf]⭐⭐⭐⭐

[7] Fang, Hao, et al. "From captions to visual concepts and back". In arXiv preprint arXiv:1411.4952, 2014. [pdf]⭐⭐⭐⭐⭐

[8] Chen, Xinlei, and C. Lawrence Zitnick. "Learning a recurrent visual representation for image caption generation". In arXiv preprint arXiv:1411.5654, 2014. [pdf]⭐⭐⭐⭐

[9] Mao, Junhua, et al. "Deep captioning with multimodal recurrent neural networks (m-rnn)". In arXiv preprint arXiv:1412.6632, 2014. [pdf]⭐⭐⭐

[10] Xu, Kelvin, et al. "Show, attend and tell: Neural image caption generation with visual attention". In arXiv preprint arXiv:1502.03044, 2015. [pdf]⭐⭐⭐⭐⭐

3.5 Machine Translation

Some milestone papers are listed in RNN / Seq-to-Seq topic.

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

[2] Sennrich, et al. "Neural Machine Translation of Rare Words with Subword Units". In arXiv preprint arXiv:1508.07909, 2015. [pdf]⭐⭐⭐

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

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

[5] Lee, et al. "Fully Character-Level Neural Machine Translation without Explicit Segmentation". In arXiv preprint arXiv:1610.03017, 2016. [pdf]⭐⭐⭐⭐⭐

[6] 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. [pdf] (Milestone) ⭐⭐⭐⭐

3.6 Robotics

[1] 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. [pdf] ⭐⭐⭐

[2] Levine, Sergey, et al. "End-to-end training of deep visuomotor policies." Journal of Machine Learning Research 17.39 (2016): 1-40. [pdf] ⭐⭐⭐⭐⭐

[3] Pinto, Lerrel, and Abhinav Gupta. "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." arXiv preprint arXiv:1509.06825 (2015). [pdf] ⭐⭐⭐

[4] 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). [pdf] ⭐⭐⭐⭐

[5] Zhu, Yuke, et al. "Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning." arXiv preprint arXiv:1609.05143 (2016). [pdf] ⭐⭐⭐⭐

[6] Yahya, Ali, et al. "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search." arXiv preprint arXiv:1610.00673 (2016). [pdf] ⭐⭐⭐⭐

[7] Gu, Shixiang, et al. "Deep Reinforcement Learning for Robotic Manipulation." arXiv preprint arXiv:1610.00633 (2016). [pdf] ⭐⭐⭐⭐

[8] 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). [pdf] ⭐⭐⭐⭐

[9] Mirowski, Piotr, et al. "Learning to navigate in complex environments." arXiv preprint arXiv:1611.03673 (2016). [pdf] ⭐⭐⭐⭐

3.7 Art

[1] Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). "Inceptionism: Going Deeper into Neural Networks". Google Research. [html] (Deep Dream) ⭐⭐⭐⭐

[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). [pdf] (Outstanding Work, most successful method currently) ⭐⭐⭐⭐⭐

[3] Zhu, Jun-Yan, et al. "Generative Visual Manipulation on the Natural Image Manifold." European Conference on Computer Vision. Springer International Publishing, 2016. [pdf] (iGAN) ⭐⭐⭐⭐

[4] Champandard, Alex J. "Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks." arXiv preprint arXiv:1603.01768 (2016). [pdf] (Neural Doodle) ⭐⭐⭐⭐

[5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Colorful Image Colorization." arXiv preprint arXiv:1603.08511 (2016). [pdf] ⭐⭐⭐⭐

[6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016). [pdf] ⭐⭐⭐⭐

[7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. "A learned representation for artistic style." arXiv preprint arXiv:1610.07629 (2016). [pdf] ⭐⭐⭐⭐

[8] Gatys, Leon and Ecker, et al."Controlling Perceptual Factors in Neural Style Transfer." arXiv preprint arXiv:1611.07865 (2016). [pdf] (control style transfer over spatial location,colour information and across spatial scale)⭐⭐⭐⭐

[9] Ulyanov, Dmitry and Lebedev, Vadim, et al. "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images." arXiv preprint arXiv:1603.03417(2016). [pdf] (texture generation and style transfer) ⭐⭐⭐⭐

[10] Yijun Li, Ming-Yu Liu ,Xueting Li, Ming-Hsuan Yang,Jan Kautz (NVIDIA). "A Closed-form Solution to Photorealistic Image Stylization." arXiv preprint arXiv:1802.06474(2018). [pdf] (Very fast and ultra realistic style transfer) ⭐⭐⭐⭐

3.8 Object Segmentation

[1] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. [pdf] ⭐⭐⭐⭐⭐

[2] 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. [pdf] ⭐⭐⭐⭐⭐

[3] Pinheiro, P.O., Collobert, R., Dollar, P. "Learning to segment object candidates." In: NIPS. 2015. [pdf] ⭐⭐⭐⭐

[4] Dai, J., He, K., Sun, J. "Instance-aware semantic segmentation via multi-task network cascades." in CVPR. 2016 [pdf] ⭐⭐⭐

[5] Dai, J., He, K., Sun, J. "Instance-sensitive Fully Convolutional Networks." arXiv preprint arXiv:1603.08678 (2016). [pdf] ⭐⭐⭐

deep-learning-papers-reading-roadmap's People

Contributors

Stargazers

Watchers

Forkers

wanjinchang 0x01111 ominux coderapes liupzone jason9263 zhihengli-ur derekdomo leezqcst luckysherry xrj-com yw3xs limingdeng iron-fe yihuang2016 whiker willie-weichi-yu duweifu kilakila-heart yanleirex guangyuzh buptweixin guoshengcv wellwang rustiron boristype000 fanshiqing junkai-ma daobilige-su shartoo wtnan2003 qzane kevin9010 hanzmyco astronana zhxxhit ieee820 wqwqwql splendor-kill diggerdu nsknsltw soledad89 gzjackcai ziqixiong khui whyou5945 vangogh0318 neutronest juniorpan stevewusin yesterdaydx eric7lau mateuszmalinowski ylpei lionktstore binbinbian ningmoon jameszhuh xzm2004260 haoshuji techstone zhaoyang1708 yult0821 absorbguo boomluo jason-az chouzhupk dengziheng hss1737 nangal lichenga2404 cnnjqzr karanchahal johndpope charlessong0 rnviswanadh rogerborras flybirdwww jdc08161063 weiningzhang brianjang luojiejian wait1988 shuyaoyimei dyhpoon woshiqchi kgl-prml agungputrap huan2016 rockystevejobs 1600 ankushgoyal migndul saurabh3949 sagaruprety sethkontny pankajpawan lgmars klgraham 0bserver07

deep-learning-papers-reading-roadmap's Issues

Paper [36] link broken

The link for the paper "Sequence to sequence learning with neural networks" [36] appears to be broken. Can you fix. Thanks.

Word2Vec is not deep learning

Sorry but I must say that Word2Vec shouldn'tbe here.

Word2Vec is a shallow (1-hidden layer) linear neural-network.

In fact, the very premise of Word2Vec is trading off model complexity (many layers, nonlinear activation) with a much simpler model that is faster to train with much larger datasets.

Omar Levy is one of the main researchers in word embeddings. See the first answer here: Quora: How does Word2Vec work?

Can you add Super Resolution papers to the list?

Graph Isomorphism

May be useful to the subject?
Graph Isomorphism in Quasipolynomial Time - László Babai arxiv

appreciation

Thank you

Request: A section for meta-learning papers

I'm a noob on meta-learning, and would like to read some papers on the topic. @floodsung , would be awesome if you could suggest some! Thanks.

Charmap error while reading README

While opening the README.md, download.py gave me this:

UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 33476: character maps to <undefined>

In case someone has the same problem, you may fix it by changing line 73 to:

with open('README.md', encoding="utf8") as readme:

What do the stars mean?

Project dependencies have API risk issues

Hi, In Deep-Learning-Papers-Reading-Roadmap, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

mistune>=0.7.2
beautifulsoup4>=4.4.1
six>=1.10.0

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project,
The version constraint of dependency mistune can be changed to >=0.1.0,<=0.8.4.

The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

The calling methods from the mistune

mistune.markdown

The calling methods from the all methods

point.text.split
clean_text
shorten_title
link.endswith
BeautifulSoup.BeautifulSoup
m2.group
time.sleep
point.find
requests.get
f.close
print
replacements.items
clean_pdf_link
get_extension
m1.group
open
f.write
readme_soup.find_all
mistune.markdown
parser.add_argument
link.replace
print_title
len
os.path.join
join
download_pdf
os.path.exists
reload
os.path.splitext
failures.append
readme.read
os.makedirs
re.search
parser.parse_args
shutil.rmtree
text.replace
argparse.ArgumentParser
sys.setdefaultencoding

@ahmaurya
Could please help me check this issue?
May I pull a request to fix it?
Thank you very much.

Inquiry about star assignment

Hey Flood,

Great work on this page!

Quick question - how do you assign stars for each paper? Is it subjective, or do you rate them by some algorithm (ie -citations/age)?

Cheers!

Is this repository maintained? How can us help you?

Hi, I am a freshman in the Deep Learning field. I love reading Deep Learning papers and your work likes the guide help me so much. But it seem so far from the last update.
Are there anyone maintain this project. If not, how can us help you?

What are awesome knowledge graph papers?

@songrotek Thank you.

Why the same model performs better in 5-way 5-shot than 5-way 1-shot ?

I really appreciate your original ideas in zhihu.When all the data can be chosen,5-shot can be better than 1-shot. 1-shot is easier than 5-shot to find the optimum weights,isn't it? So i think 1-shot can be better than 5-shot,but it not.Can you give me some advice?

dl

update the roadmap

hi, I think that the roadmap should add more papers. There are few papers published in 2017.

Wow!

This is really great!

Suggestion: Add Algorithmic Learning Section?

There have been amazing papers in this area starting from last year and it would be awesome if it could be added. I'm thinking specifically of papers beyond NTMs, such as NPI, Neural RAM, Neural GPU, Neural Programmer, etc.

Importing into Mendeley

I know this isn't really an issue, but since this is a repo about reading papers, I thought someone might be able to help me.

Is there an easy way to just grab a list of papers like this and import them into Mendeley? Ideally so that I can keep the order so that I can read them in the same order.

Is this repo still alive?

Hi, I followed this repository a year ago. This is definitely a good work for giving those who want do get into this field an introduction.

However, I noticed that the latest commit and merged pull request were dated in one year ago when I go back to check if there is some updates for recent published articles.

Is this repo still alive? If no, is there other repository recommended for up-to-date paper list?

Suggestion: add paper "Approximation Capabilities of Muitilayer Feedforward Networks"

link to paper: http://zmjones.com/static/statistical-learning/hornik-nn-1991.pdf

Faster RCNN pdf broken link

UnicodeEncodeError: 'charmap' codec can't encode

[3] "Reducing the dimensionality of data with neur [...] (http://www.cs.toronto.edu/~hinton/science.pdf)
Traceback (most recent call last):
File "download.py", line 102, in
print_title(point.text)
File "download.py", line 50, in print_title
print('\n'.join(("", title, pattern * len(title))))
File "C:\Python27\lib\encodings\cp437.py", line 12, in encode
return codecs.charmap_encode(input,errors,encoding_map)
UnicodeEncodeError: 'charmap' codec can't encode character u'\uff08' in position 23: character maps to

Fix quotation in README.md

I got this error:

Traceback (most recent call last):
File "c:\Users\jshat\Documents\Code\Machine Learning\Deep-Learning-Papers-Reading-Roadmap\download.py", line 88, in
readme_html = mistune.markdown(readme.read())
File "C:\Python37\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 33411: character maps to

Due to the use of “” instead of "" in line 325 of README.md

line 325 (modified):
[1] J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation." in CVPR, 2015. [pdf] ⭐⭐⭐⭐⭐

suggestion： Add speech recognition in application part

I think Speech recognition should also be in the list

Suggestion: WaveNet

I think the WaveNet paper by DeepMind is worth including. Maybe under "Applications/Audio"?

YOLO

Whee is the YOLO paper?

Missing ' on line 87

There is a missing at the end of 'utf8
with open('README.md',encoding='utf8) as readme:

Import into EndNote

If the citations can be import into EndNote directly, it would be more helpful.
Thank you.

Suggestion for 3.8 Audio section

I think this paper is worth reading:

Learning to Communicate with Deep Multi-Agent Reinforcement Learning:
https://arxiv.org/pdf/1605.06676v2.pdf
1605.06676v2.pdf

Adding Documentation in README.md

We can add in documentation to use the code in the README.md section.

Suggestions about DL for Knowledge.

I think the deep learning techniques for knowledge graph, especially for relation extraction are missing. The author is suggested to give some suggestions for these directions. :)

suggestion for optimization

Adam: A Method for Stochastic Optimization
https://arxiv.org/abs/1412.6980

Thank you!!

It is very wonderful!!!!

pirated answer

I found there was an answer almost the same as the repo md：深度学习入门必看的书和论文？有哪些必备的技能需学习？- 景略集智的回答 - 知乎
which made me very angry😡

Suggestion for Deep Learning for Finance portion

Anyone has suggested papers of state-of-the-art for apply deep learning to finance? Algorithmic trading, trade execution, risk modelling, using deep reinforcement learning etc.

how about Pyramid Scene Parsing Network

hope review this article: Pyramid Scene Parsing Network

paper [52] link broken

I was trying to access the paper [52] Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." but the link is broken. 😞

Deep learning book link has moved

The new address is http://www.deeplearningbook.org/

The download.py script needs an extra "encoding='utf-8'" at line87 or else it won't work on Windows computers

Original line 87:
with open('README.md') as readme:

Corrected version of line 87:
with open('README.md','r',encoding='utf-8') as readme:

Explanation:
Windows uses GBK to decode rather than utf-8 at default setting

Deep Learning for Software Engineers?

Thanks for your theoretical approach.
Currently, I am making: Machine Learning for Software Engineers (https://github.com/ZuzooVn/machine-learning-for-software-engineers)

Did you have any suggestion for Deep Learning's Top-down learning path for Software Engineers ?

Thank you

Missing apostrophe on line 87

There is a missing at the end of 'utf8
with open('README.md',encoding='utf8) as readme:

extra resourses

Hi
i added your repo link to my spreadsheet
you can check it if you have any suggestion
https://docs.google.com/spreadsheets/d/13QQinPFhU9DwujctXS0A7un0up4N5BNyUDZwxK4I6hg/edit#gid=1588925720
Thanks

I cannot thank you enough

Suggestion for Computational Biology Part

I think these repositories are worth looking at

https://github.com/gokceneraslan/awesome-deepbio (Especially this one I find really useful)
https://github.com/pimentel/deep_learning_papers

[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book. (2015). [pdf] (Deep Learning Bible, you can read this book while reading following papers.) ⭐⭐⭐⭐⭐
[2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554. [pdf](Deep Learning Eve) ⭐⭐⭐
[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507. [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐
[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. [pdf] (AlexNet, Deep Learning Breakthrough) ⭐⭐⭐⭐⭐
[5] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). [pdf] (VGGNet,Neural Networks become very deep!) ⭐⭐⭐
[6] Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [pdf] (GoogLeNet) ⭐⭐⭐
[8] Hinton, Geoffrey, et al. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." IEEE Signal Processing Magazine 29.6 (2012): 82-97. [pdf] (Breakthrough in speech recognition):star::star::star::star:
[9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. "Speech recognition with deep recurrent neural networks." 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (RNN):star::star::star:
[11] Sak, Haşim, et al. "Fast and accurate recurrent neural network acoustic models for speech recognition." arXiv preprint arXiv:1507.06947 (2015). [pdf] (Google Speech Recognition System) ⭐⭐⭐
[13] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig "Achieving Human Parity in Conversational Speech Recognition." arXiv preprint arXiv:1610.05256 (2016). [pdf] (State-of-the-art in speech recognition, Microsoft) ⭐⭐⭐⭐
[14] Hinton, Geoffrey E., et al. "Improving neural networks by preventing co-adaptation of feature detectors." arXiv preprint arXiv:1207.0580 (2012). [pdf] (Dropout) ⭐⭐⭐
[15] Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958. [pdf] ⭐⭐⭐
[16] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015). [pdf] (An outstanding Work in 2015) ⭐⭐⭐⭐
[18] Courbariaux, Matthieu, et al. "Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1." [pdf] (New Model,Fast) ⭐⭐⭐
[20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. "Net2net: Accelerating learning via knowledge transfer." arXiv preprint arXiv:1511.05641 (2015). [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐
[25] 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). [pdf] (ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup) ⭐⭐⭐⭐⭐
[26] Iandola, Forrest N., et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." arXiv preprint arXiv:1602.07360 (2016). [pdf] (Also a new direction to optimize NN,DeePhi Tech Startup) ⭐⭐⭐⭐
[27] Le, Quoc V. "Building high-level features using large scale unsupervised learning." 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat) ⭐⭐⭐⭐
[30] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). [pdf] (DCGAN) ⭐⭐⭐⭐
[35] Cho, Kyunghyun, et al. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014). [pdf] (First Seq-to-Seq Paper) ⭐⭐⭐⭐
[36] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural information processing systems. 2014. [pdf] (Outstanding Work) ⭐⭐⭐⭐⭐
[37] Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. "Neural Machine Translation by Jointly Learning to Align and Translate." arXiv preprint arXiv:1409.0473 (2014). [pdf] ⭐⭐⭐⭐
[47] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. "Dueling network architectures for deep reinforcement learning." arXiv preprint arXiv:1511.06581 (2015). [pdf] (ICLR best paper,great idea) ⭐⭐⭐⭐
[53] Bengio, Yoshua. "Deep Learning of Representations for Unsupervised and Transfer Learning." ICML Unsupervised and Transfer Learning 27 (2012): 17-36. [pdf] (A Tutorial) ⭐⭐⭐
[54] Silver, Daniel L., Qiang Yang, and Lianghao Li. "Lifelong Machine Learning Systems: Beyond Learning Algorithms." AAAI Spring Symposium: Lifelong Machine Learning. 2013. [pdf] (A brief discussion about lifelong learning) ⭐⭐⭐
[57] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. "Actor-mimic: Deep multitask and transfer reinforcement learning." arXiv preprint arXiv:1511.06342 (2015). [pdf] (RL domain) ⭐⭐⭐
[59] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. "Human-level concept learning through probabilistic program induction." Science 350.6266 (2015): 1332-1338. [pdf] (No Deep Learning,but worth reading) ⭐⭐⭐⭐⭐
[1] Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. "Deep neural networks for object detection." Advances in Neural Information Processing Systems. 2013. [pdf] ⭐⭐⭐
[2] 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. [pdf] (RCNN) ⭐⭐⭐⭐⭐
[3] He, Kaiming, et al. "Spatial pyramid pooling in deep convolutional networks for visual recognition." European Conference on Computer Vision. Springer International Publishing, 2014. [pdf] (SPPNet) ⭐⭐⭐⭐
[5] Ren, Shaoqing, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015. [pdf] ⭐⭐⭐⭐
[1] Wang, Naiyan, and Dit-Yan Yeung. "Learning a deep compact image representation for visual tracking." Advances in neural information processing systems. 2013. [pdf] (First Paper to do visual tracking using Deep Learning,DLT Tracker) ⭐⭐⭐
[3] Wang, Lijun, et al. "Visual tracking with fully convolutional networks." Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] (FCNT) ⭐⭐⭐⭐
[4] Held, David, Sebastian Thrun, and Silvio Savarese. "Learning to Track at 100 FPS with Deep Regression Networks." arXiv preprint arXiv:1604.01802 (2016). [pdf] (GOTURN,Really fast as a deep learning method,but still far behind un-deep-learning methods) ⭐⭐⭐⭐
[6] Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. "Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." ECCV (2016) [pdf] (C-COT) ⭐⭐⭐⭐
[7] Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." arXiv preprint arXiv:1608.07242 (2016). [pdf] (VOT2016 Winner,TCNN) ⭐⭐⭐⭐
[1] Farhadi,Ali,etal. "Every picture tells a story: Generating sentences from images". In Computer VisionECCV 2010. Springer Berlin Heidelberg:15-29, 2010. [pdf] ⭐⭐⭐
[4] Donahue, Jeff, et al. "Long-term recurrent convolutional networks for visual recognition and description". In arXiv preprint arXiv:1411.4389 ,2014. [pdf]
[5] Karpathy, Andrej, and Li Fei-Fei. "Deep visual-semantic alignments for generating image descriptions". In arXiv preprint arXiv:1412.2306, 2014. [pdf]:star::star::star::star::star:
[6] 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. [pdf]:star::star::star::star:
[8] Chen, Xinlei, and C. Lawrence Zitnick. "Learning a recurrent visual representation for image caption generation". In arXiv preprint arXiv:1411.5654, 2014. [pdf]:star::star::star::star:
[10] Xu, Kelvin, et al. "Show, attend and tell: Neural image caption generation with visual attention". In arXiv preprint arXiv:1502.03044, 2015. [pdf]:star::star::star::star::star:
[3] Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Effective approaches to attention-based neural machine translation." arXiv preprint arXiv:1508.04025 (2015). [pdf] ⭐⭐⭐⭐
[4] Chung, et al. "A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation". In arXiv preprint arXiv:1603.06147, 2016. [pdf]:star::star:
[6] 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. [pdf] (Milestone) ⭐⭐⭐⭐
[1] 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. [pdf] ⭐⭐⭐
[3] Pinto, Lerrel, and Abhinav Gupta. "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." arXiv preprint arXiv:1509.06825 (2015). [pdf] ⭐⭐⭐
[4] 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). [pdf] ⭐⭐⭐⭐
[5] Zhu, Yuke, et al. "Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning." arXiv preprint arXiv:1609.05143 (2016). [pdf] ⭐⭐⭐⭐
[6] Yahya, Ali, et al. "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search." arXiv preprint arXiv:1610.00673 (2016). [pdf] ⭐⭐⭐⭐
[8] 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). [pdf] ⭐⭐⭐⭐
[3] Zhu, Jun-Yan, et al. "Generative Visual Manipulation on the Natural Image Manifold." European Conference on Computer Vision. Springer International Publishing, 2016. [pdf] (iGAN) ⭐⭐⭐⭐
[6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016). [pdf] ⭐⭐⭐⭐
[7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. "A learned representation for artistic style." arXiv preprint arXiv:1610.07629 (2016). [pdf] ⭐⭐⭐⭐

Download no error but all 0 bytes

I am using the script under python 3.6.1 environment. It appears that no error occurred but all downloaded file are 0 bytes.

Wonder why this happened.

Few suggestions for papers

'Densely Connected Convolutional Networks' - G Huang, GB Huang, S Song, K You
obtained state-of-the-art results in image classification on CIFAR-10, CIFAR-100, SVHN, ImageNet
This paper also won CVPR 17 best paper award along with Apple's paper
'U-Net: Convolutional Networks for Biomedical Image Segmentation' - O Ronneberger, P Fischer, T Brox
This paper is quite successful in medical image segmentation and enjoyed success in Kaggle too
'The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation' -
obtained state-of-the-art results in segmentation on CamVid and Gatech datasets. This one is by Bengio group
'Wasserstein GAN' - M Arjovsky, S Chintala, L Bottou
This paper solved a lot of issues with GANs stability. The change in loss function and training paradigm
make loss functions interpretable. They obtained good results even with a normal mlp on LSUN dataset.

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.