作者：floodsun

编译：ronghuaiyang

这是作者一年前整理的东西，有些最新的论文没有包含进去，但是对于新手来说，入门足够了！

如果你是深度学习领域的新人，你的第一个问题可能是“我该从哪些论文开始读起呢？”这就是深度学习论文的阅读路线图！

这个路线图是根据下面几个规则构建的：

• 从概要到细节
• 从老的到最新的业界领先
• 从通用的到细分领域的
• 聚焦业界领先的

你会发现很多论文很新，但是确实值得一读。

我会继续在这个路线图上添加论文。

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1 深度学习历史和基础

1.0 书

[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. “Deep learning.” An MIT Press book. (2015). [html] (深度学习的圣经，你读这本书的同时可以读下面的论文.)⭐️⭐️⭐️⭐️⭐️

1.1 综述

[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015): 436-444. [pdf] (三巨头的综述) ⭐️⭐️⭐️⭐️⭐️

1.2 深度置信网络(DBN)(深度学习黎明之前的里程碑)

[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](深度学习黎明之前) ⭐️⭐️⭐️

[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. “Reducing the dimensionality of data with neural networks.” Science 313.5786 (2006): 504-507. [pdf] (里程碑, 展示了深度学习的前途) ⭐️⭐️⭐️

1.3 ImageNet的演进（深度学习从这里突破）

[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, 深度学习的突破)⭐️⭐️⭐️⭐️⭐️

[5] Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014). [pdf] (VGGNet,神经网络开始变得很深!) ⭐️⭐️⭐️

[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,非常非常深的神经网络, CVPR最佳论文)⭐️⭐️⭐️⭐️⭐️

1.4 语音识别的演进

[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] (语音识别的突破)⭐️⭐️⭐️⭐️

[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，循环神经网络)⭐️⭐️⭐️

[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的语音识别系统) ⭐️⭐️⭐️

[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的语音识别系统)⭐️⭐️⭐️⭐️

[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的语音识别, Microsoft)⭐️⭐️⭐️⭐️

读过了上面的论文之后，你应该对深度学习的历史，基本的深度学习模型的结构（包括CNN，RNN，LSTM）有了个基本的理解，包括深度学习是如何应用到图像和语音的识别的问题中的。下面的论文将带你更加深入的理解深度学习方法，深度学习在不同领域的应用和前沿研究。我建议你可以根据你的兴趣和研究方向选择性的阅读下面的论文。

2 深度学习方法

2.1 模型

[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] (2015年的杰出工作) ⭐️⭐️⭐️⭐️

[17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. “Layer normalization.” arXiv preprint arXiv:1607.06450 (2016). [pdf] (Batch Normalization的升级版) ⭐️⭐️⭐️⭐️

[18] Courbariaux, Matthieu, et al. “Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1.” [pdf] (新模型，速度快) ⭐️⭐️⭐️

[19] Jaderberg, Max, et al. “Decoupled neural interfaces using synthetic gradients.” arXiv preprint arXiv:1608.05343 (2016). [pdf] (训练方法的创新，了不起的工作)⭐️⭐️⭐️⭐️⭐️

[20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. “Net2net: Accelerating learning via knowledge transfer.” arXiv preprint arXiv:1511.05641 (2015). [pdf] (通过修改之前的训练过的网络来减少训练的epoch数量) ⭐️⭐️⭐️

[21] Wei, Tao, et al. “Network Morphism.” arXiv preprint arXiv:1603.01670 (2016). [pdf] (通过修改之前的训练过的网络来减少训练的epoch数量) ⭐️⭐️⭐️

2.2 优化方法

[22] Sutskever, Ilya, et al. “On the importance of initialization and momentum in deep learning.” ICML (3) 28 (2013): 1139-1147. [pdf] (动量优化器) ⭐️⭐️

[23] Kingma, Diederik, and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014). [pdf] (可能是最常用的优化器) ⭐️⭐️⭐️

[24] Andrychowicz, Marcin, et al. “Learning to learn by gradient descent by gradient descent.” arXiv preprint arXiv:1606.04474 (2016). [pdf] (神经元优化器，杰出的工作)⭐️⭐️⭐️⭐️⭐️

[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 最佳论文, 是神经网络运行更快的新方向)⭐️⭐️⭐️⭐️⭐️

[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] (也是一个优化神经网络的新方向) ⭐️⭐️⭐️⭐️

2.3 非监督学习/深度生成模型

谷歌[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] (里程碑, 吴恩达, 谷歌大脑项目, 认猫)⭐️⭐️⭐️⭐️

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

[29] Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in Neural Information Processing Systems. 2014. [pdf](生成对抗网络，超酷的点子) ⭐️⭐️⭐️⭐️⭐️

[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) ⭐️⭐️⭐️⭐️

[31] Gregor, Karol, et al. “DRAW: A recurrent neural network for image generation.” arXiv preprint arXiv:1502.04623 (2015). [pdf] (带注意力机制的VAE, 出色的工作)⭐️⭐️⭐️⭐️⭐️

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

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

2.4 循环神经网络（RNN）/ 序列到序列（Seq-to-Seq）的模型

[34] Graves, Alex. “Generating sequences with recurrent neural networks.” arXiv preprint arXiv:1308.0850 (2013). [pdf](LSTM, 非常好的生成结果, 展示了RNN的威力)⭐️⭐️⭐️⭐️

[35] Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014). [pdf] (第一个Seq-to-Seq 的论文) ⭐️⭐️⭐️⭐️

[36] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. “Sequence to sequence learning with neural networks.” Advances in neural information processing systems. 2014. [pdf] (杰出的工作) ⭐️⭐️⭐️⭐️⭐️

[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] ⭐️⭐️⭐️⭐️

[38] Vinyals, Oriol, and Quoc Le. “A neural conversational model.” arXiv preprint arXiv:1506.05869 (2015). [pdf] (Seq-to-Seq在聊天机器人上应用) ⭐️⭐️⭐️

2.5 神经元图灵机

[39] Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014). [pdf](未来计算机的基本原型) ⭐️⭐️⭐️⭐️⭐️

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

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

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

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

[44] Graves, Alex, et al. “Hybrid computing using a neural network with dynamic external memory.” Nature (2016). [pdf](里程碑，综合了上面这些论文的思想)⭐️⭐️⭐️⭐️⭐️

2.6 深度强化学习

[45] Mnih, Volodymyr, et al. “Playing atari with deep reinforcement learning.” arXiv preprint arXiv:1312.5602 (2013). [pdf]) (第一篇命名为深度强化学习的论文) ⭐️⭐️⭐️⭐️

[46] Mnih, Volodymyr, et al. “Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529-533. [pdf] (里程碑) ⭐️⭐️⭐️⭐️⭐️

[47] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. “Dueling network architectures for deep reinforcement learning.” arXiv preprint arXiv:1511.06581 (2015). [pdf] (ICLR最佳论文，非常好的点子) ⭐️⭐️⭐️⭐️

[48] Mnih, Volodymyr, et al. “Asynchronous methods for deep reinforcement learning.” arXiv preprint arXiv:1602.01783 (2016). [pdf] (业界领先的方法) ⭐️⭐️⭐️⭐️⭐️

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

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

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

[52] Silver, David, et al. “Mastering the game of Go with deep neural networks and tree search.” Nature 529.7587 (2016): 484-489. [pdf] (阿尔法狗) ⭐️⭐️⭐️⭐️⭐️

2.7 深度迁移学习 / 终身学习 / 特别针对是强化学习

[53] Bengio, Yoshua. “Deep Learning of Representations for Unsupervised and Transfer Learning.” ICML Unsupervised and Transfer Learning 27 (2012): 17-36. [pdf] (一个教程) ⭐️⭐️⭐️

[54] Silver, Daniel L., Qiang Yang, and Lianghao Li. “Lifelong Machine Learning Systems: Beyond Learning Algorithms.” AAAI Spring Symposium: Lifelong Machine Learning. 2013. [pdf] (一个关于终身学习的简单的探讨) ⭐️⭐️⭐️

[55] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. “Distilling the knowledge in a neural network.” arXiv preprint arXiv:1503.02531 (2015). [pdf] (深度学习教父的工作)⭐️⭐️⭐️⭐️

[56] Rusu, Andrei A., et al. “Policy distillation.” arXiv preprint arXiv:1511.06295 (2015). [pdf] (强化学习的领域) ⭐️⭐️⭐️

[57] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. “Actor-mimic: Deep multitask and transfer reinforcement learning.” arXiv preprint arXiv:1511.06342 (2015). [pdf] (强化学习的领域) ⭐️⭐️⭐️

[58] Rusu, Andrei A., et al. “Progressive neural networks.” arXiv preprint arXiv:1606.04671 (2016). [pdf] (了不起的工作, 新的想法) ⭐️⭐️⭐️⭐️⭐️

2.8 单样本学习

[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] (不是深度学习，但是值得一读)⭐️⭐️⭐️⭐️⭐️

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

[61] Santoro, Adam, et al. “One-shot Learning with Memory-Augmented Neural Networks.” arXiv preprint arXiv:1605.06065 (2016). [pdf] (一个单样本学习的基本步骤)⭐️⭐️⭐️⭐️

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

[63] Hariharan, Bharath, and Ross Girshick. “Low-shot visual object recognition.” arXiv preprint arXiv:1606.02819 (2016). [pdf] ⭐️⭐️⭐️⭐️

3 应用

3.1 自然语言处理(NLP)

[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] (词向量) ⭐️⭐️⭐️

[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] (问答任务)⭐️⭐️⭐️

[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] (文本分类的state-of-the-art的工作) ⭐️⭐️⭐️

[9] Armand Joulin, et al. “Bag of Tricks for Efficient Text Classification.” arXiv preprint arXiv:1607.01759(2016) [pdf] (比state-of-the-art差点, 但是快很多) ⭐️⭐️⭐️

3.2 物体检测

[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，实用) ⭐️⭐️⭐️⭐️⭐️

[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] ⭐️⭐️⭐️⭐️

3.3 视觉跟踪

[1] Wang, Naiyan, and Dit-Yan Yeung. “Learning a deep compact image representation for visual tracking.” Advances in neural information processing systems. 2013. [pdf] (第一篇使用深度学习来进行视觉跟踪的论文，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,作为深度学习的方法已经是很快的了，但是还是落后于非深度学习的方法)⭐️⭐️⭐️⭐️

[5] Bertinetto, Luca, et al. “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016). [pdf] (SiameseFC,新的实时物体跟踪方面的state-of-the-art ) ⭐️⭐️⭐️⭐️

[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 图像描述

[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 机器翻译

一些里程碑的论文列在RNN / Seq-to-Seq 主题下面。

[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] (里程碑) ⭐️⭐️⭐️⭐️

3.6 机器人

[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 艺术风格

[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] (杰出的工作, 目前最成功的工作) ⭐️⭐️⭐️⭐️⭐️

[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] (控制空间上的风格转换，颜色信息以及空间尺度)⭐️⭐️⭐️⭐️

[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] (纹理生成和风格转换) ⭐️⭐️⭐️⭐️

3.8 物体分割

[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]⭐️⭐️⭐️

原文链接：https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md