128篇21大领域必读论文【转】
2019-03-01 本文已影响0人
武耀文
这份阅读列表的组织原则是这样的：
从全局到枝干： 从综述类、全局性的文章到细分领域的具体论文。
从经典到最前沿： 每个话题的文章都是按时间顺序来排的，可以清晰给出每个方向的发展脉络。
从通用理论到具体应用： 有些论文是针对深度学习通用理论的，有些论文章则针对具体的应用领域。
专注于最先进的研究： 收集有许多最新论文，保证阅读列表的时效性。
当然，这里的每个话题都只选几篇最具代表性的论文，深入研究的话，还需要更进一步的阅读。
基于这些论文的影响力，你会发现很多新近发表的文章也值得一读。 此外，这份阅读列表在原文页面会不断更新，值得你时时备查。
https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
想一键打包下载所有的论文？没问题， AI科技大本营已经给你准备好了懒人专属通道。请在公众号会话回复“ 路径 ”，即可获取本文所有论文PDF资料。
- 深度学习基础及历史
1.0 书
[0] 深度学习圣经 ★★★★★
Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. “Deep learning.” An MIT Press book. (2015).
https://github.com/HFTrader/DeepLearningBook/raw/master/DeepLearningBook.pdf
1.1 报告
[1] 三巨头报告 ★★★★★
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015): 436-444.
http://www.cs.toronto.edu/%7Ehinton/absps/NatureDeepReview.pdf
1.2 深度信念网络 (DBN)
[2] 深度学习前夜的里程碑 ★★★
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/%7Ehinton/absps/ncfast.pdf
[3] 展示深度学习前景的里程碑 ★★★
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/%7Ehinton/science.pdf
1.3 ImageNet革命（深度学习大爆炸）
[4] AlexNet的深度学习突破 ★★★
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
[5] VGGNet深度神经网络出现 ★★★
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
[6] GoogLeNet ★★★
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
[7] ResNet极深度神经网络，CVPR最佳论文 ★★★★★
He, Kaiming, et al. “Deep residual learning for image recognition.” arXiv preprint arXiv:1512.03385 (2015).
https://arxiv.org/pdf/1512.03385.pdf
1.4 语音识别革命
[8] 语音识别突破 ★★★★
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.
http://cs224d.stanford.edu/papers/maas_paper.pdf
[9] RNN论文 ★★★
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.
http://arxiv.org/pdf/1303.5778.pdf
[10] 端对端RNN语音识别 ★★★
Graves, Alex, and Navdeep Jaitly. “Towards End-To-End Speech Recognition with Recurrent Neural Networks.” ICML. Vol. 14. 2014.
http://www.jmlr.org/proceedings/papers/v32/graves14.pdf
[11] Google语音识别系统论文 ★★★
Sak, Haşim, et al. “Fast and accurate recurrent neural network acoustic models for speech recognition.” arXiv preprint arXiv:1507.06947 (2015).
http://arxiv.org/pdf/1507.06947
[12] 百度语音识别系统论文 ★★★★
Amodei, Dario, et al. “Deep speech 2: End-to-end speech recognition in english and mandarin.” arXiv preprint arXiv:1512.02595 (2015).
https://arxiv.org/pdf/1512.02595.pdf
[13] 来自微软的当下最先进的语音识别论文 ★★★★
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).
https://arxiv.org/pdf/1610.05256v1
读完上面这些论文，你将对 深度学习的历史、深度学习模型（CNN、RNN、LSTM等）的基本架构 有一个基本认识， 并能理解深度学习是如何解决图像及语音识别问题的 。接下来的 论文将 带你深入理解深度学习方法、深度学习在前沿领域的不同应用。根据自己的兴趣和研究方向选择阅读即可： - 深度学习方法
2.1 模型
[14] Dropout ★★★
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
[15] 过拟合 ★★★
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.old/source/srivastava14a.pdf
[16] Batch归一化——2015年杰出成果 ★★★★
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
[17] Batch归一化的升级 ★★★★
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
[18] 快速训练新模型 ★★★
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
[19] 训练方法创新 ★★★★★
Jaderberg, Max, et al. “Decoupled neural interfaces using synthetic gradients.” arXiv preprint arXiv:1608.05343 (2016).
https://arxiv.org/pdf/1608.05343
[20] 修改预训练网络以降低训练耗时 ★★★
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
[21] 修改预训练网络以降低训练耗时 ★★★
Wei, Tao, et al. “Network Morphism.” arXiv preprint arXiv:1603.01670 (2016).
https://arxiv.org/abs/1603.01670
2.2 优化
[22] 动量优化器 ★★
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
[23] 可能是当前使用最多的随机优化 ★★★
Kingma, Diederik, and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).
http://arxiv.org/pdf/1412.6980
[24] 神经优化器 ★★★★★
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
[25] ICLR最佳论文，让神经网络运行更快的新方向 ★★★★★
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
[26] 优化神经网络的另一个新方向 ★★★★
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
2.3 无监督学习 / 深度生成式模型
[27] Google Brain找猫的里程碑论文，吴恩达 ★★★★
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
[28] 变分自编码机 (VAE) ★★★★
Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).
http://arxiv.org/pdf/1312.6114
[29] 生成式对抗网络 (GAN) ★★★★★
Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in Neural Information Processing Systems. 2014.
http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
[30] 解卷积生成式对抗网络 (DCGAN) ★★★★
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
[31] Attention机制的变分自编码机 ★★★★★
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
[32] PixelRNN ★★★★
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
[33] PixelCNN ★★★★
Oord, Aaron van den, et al. “Conditional image generation with PixelCNN decoders.” arXiv preprint arXiv:1606.05328 (2016).
https://arxiv.org/pdf/1606.05328
2.4 RNN / 序列到序列模型
[34] RNN的生成式序列，LSTM ★★★★
Graves, Alex. “Generating sequences with recurrent neural networks.” arXiv preprint arXiv:1308.0850 (2013).
http://arxiv.org/pdf/1308.0850
[35] 第一份序列到序列论文 ★★★★
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
[36] 神经网络的序列到序列学习 ★★★★★
Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. “Sequence to sequence learning with neural networks.” Advances in neural information processing systems. 2014.
http://papers.nips.cc/paper/5346-information-based-learning-by-agents-in-unbounded-state-spaces.pdf
[37] 神经机器翻译 ★★★★
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
[38] 序列到序列Chatbot ★★★
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
2.5 神经网络图灵机
[39] 未来计算机的基本原型 ★★★★★
Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014).
http://arxiv.org/pdf/1410.5401.pdf
[40] 强化学习神经图灵机 ★★★
Zaremba, Wojciech, and Ilya Sutskever. “Reinforcement learning neural Turing machines.” arXiv preprint arXiv:1505.00521 362 (2015).
https://pdfs.semanticscholar.org/f10e/071292d593fef939e6ef4a59baf0bb3a6c2b.pdf
[41] 记忆网络 ★★★
Weston, Jason, Sumit Chopra, and Antoine Bordes. “Memory networks.” arXiv preprint arXiv:1410.3916 (2014).
http://arxiv.org/pdf/1410.3916
[42] 端对端记忆网络 ★★★★
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
[43] 指针网络 ★★★★
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
[44] 整合神经网络图灵机概念的里程碑论文 ★★★★★
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
2.6 深度强化学习
[45] 第一篇以深度强化学习为名的论文 ★★★★
Mnih, Volodymyr, et al. “Playing atari with deep reinforcement learning.” arXiv preprint arXiv:1312.5602 (2013).
http://arxiv.org/pdf/1312.5602.pdf
[46] 里程碑 ★★★★★
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
[47] ICLR最佳论文 ★★★★
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
[48] 当前最先进的深度强化学习方法 ★★★★★
Mnih, Volodymyr, et al. “Asynchronous methods for deep reinforcement learning.” arXiv preprint arXiv:1602.01783 (2016).
http://arxiv.org/pdf/1602.01783
[49] DDPG ★★★★
Lillicrap, Timothy P., et al. “Continuous control with deep reinforcement learning.” arXiv preprint arXiv:1509.02971 (2015).
http://arxiv.org/pdf/1509.02971
[50] NAF ★★★★
Gu, Shixiang, et al. “Continuous Deep Q-Learning with Model-based Acceleration.” arXiv preprint arXiv:1603.00748 (2016).
http://arxiv.org/pdf/1603.00748
[51] TRPO ★★★★
Schulman, John, et al. “Trust region policy optimization.” CoRR, abs/1502.05477 (2015).
http://www.jmlr.org/proceedings/papers/v37/schulman15.pdf
[52] AlphaGo ★★★★★
Silver, David, et al. “Mastering the game of Go with deep neural networks and tree search.” Nature 529.7587 (2016): 484-489.
http://willamette.edu/%7Elevenick/cs448/goNature.pdf
2.7 深度迁移学习 / 终生学习 / 强化学习
[53] Bengio教程 ★★★
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
[54] 终生学习的简单讨论 ★★★
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
[55] Hinton、Jeff Dean大神研究 ★★★★
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
[56] 强化学习策略 ★★★
Rusu, Andrei A., et al. “Policy distillation.” arXiv preprint arXiv:1511.06295 (2015).
http://arxiv.org/pdf/1511.06295
[57] 多任务深度迁移强化学习 ★★★
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
[58] 累进式神经网络 ★★★★★
Rusu, Andrei A., et al. “Progressive neural networks.” arXiv preprint arXiv:1606.04671 (2016).
https://arxiv.org/pdf/1606.04671
2.8 一次性深度学习
[59] 不涉及深度学习，但值得一读 ★★★★★
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
[60] 一次性图像识别 ★★★
Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. “Siamese Neural Networks for One-shot Image Recognition.”(2015)
http://www.cs.utoronto.ca/%7Egkoch/files/msc-thesis.pdf
[61] 一次性学习基础 ★★★★
Santoro, Adam, et al. “One-shot Learning with Memory-Augmented Neural Networks.” arXiv preprint arXiv:1605.06065 (2016).
http://arxiv.org/pdf/1605.06065
[62] 一次性学习网络 ★★★
Vinyals, Oriol, et al. “Matching Networks for One Shot Learning.” arXiv preprint arXiv:1606.04080 (2016).
https://arxiv.org/pdf/1606.04080
[63] 大型数据 ★★★★
Hariharan, Bharath, and Ross Girshick. “Low-shot visual object recognition.” arXiv preprint arXiv:1606.02819 (2016).
http://arxiv.org/pdf/1606.02819 - 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)
https://www.hds.utc.fr/%7Ebordesan/dokuwiki/lib/exe/fetch.php?id=en%3Apubli&cache=cache&media=en:bordes12aistats.pdf
[2] ★★★
word2vec
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
[3] ★★★
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
[4] ★★★★
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
[5] ★★★★
Yoon Kim, et al. “Character-Aware Neural Language Models.” NIPS(2015) arXiv preprint arXiv:1508.06615(2015)
https://arxiv.org/abs/1508.06615
[6] bAbI任务 ★★★
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
[7] CNN / DailyMail 风格对比 ★★
Karl Moritz Hermann, et al. “Teaching Machines to Read and Comprehend.” arXiv preprint arXiv:1506.03340(2015)
https://arxiv.org/abs/1506.03340
[8] 当前最先进的文本分类 ★★★
Alexis Conneau, et al. “Very Deep Convolutional Networks for Natural Language Processing.” arXiv preprint arXiv:1606.01781(2016)
https://arxiv.org/abs/1606.01781
[9] 稍次于最先进方案，但速度快很多 ★★★
Armand Joulin, et al. “Bag of Tricks for Efficient Text Classification.” arXiv preprint arXiv:1607.01759(2016)
https://arxiv.org/abs/1607.01759
3.2 目标检测
[1] ★★★
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
[2] RCNN ★★★ ★★
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
[3] SPPNet ★★★ ★
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
[4] ★★★ ★
Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE International Conference on Computer Vision. 2015.
https://pdfs.semanticscholar.org/8f67/64a59f0d17081f2a2a9d06f4ed1cdea1a0ad.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.
http://papers.nips.cc/paper/5638-analysis-of-variational-bayesian-latent-dirichlet-allocation-weaker-sparsity-than-map.pdf
[6] 相当实用的YOLO项目 ★★★ ★★
Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” arXiv preprint arXiv:1506.02640 (2015).
http://homes.cs.washington.edu/%7Eali/papers/YOLO.pdf
[7] ★★★
Liu, Wei, et al. “SSD: Single Shot MultiBox Detector.” arXiv preprint arXiv:1512.02325 (2015).
http://arxiv.org/pdf/1512.02325
[8] ★★★ ★
Dai, Jifeng, et al. “R-FCN: Object Detection via Region-based Fully Convolutional Networks.” arXiv preprint arXiv:1605.06409 (2016).
https://arxiv.org/abs/1605.06409
[9] ★★★ ★
He, Gkioxari, et al. “Mask R-CNN” arXiv preprint arXiv:1703.06870 (2017).
https://arxiv.org/abs/1703.06870
3.3 视觉追踪
[1] 第一份采用深度学习的视觉追踪论文，DLT追踪器 ★★★
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
[2] SO-DLT ★★★ ★
Wang, Naiyan, et al. “Transferring rich feature hierarchies for robust visual tracking.” arXiv preprint arXiv:1501.04587 (2015).
http://arxiv.org/pdf/1501.04587
[3] FCNT ★★★ ★
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
[4] 跟深度学习一样快的非深度学习方法，GOTURN ★★★ ★
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
[5] 新的最先进的实时目标追踪方案 SiameseFC ★★★ ★
Bertinetto, Luca, et al. “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016).
https://arxiv.org/pdf/1606.09549
[6] C-COT ★★★ ★
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
[7] VOT2016大赛冠军 TCNN ★★★ ★
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
3.4 图像标注
[1] ★★★
Farhadi,Ali,etal. “Every picture tells a story: Generating sentences from images”. In Computer VisionECCV 201 match 0. Sp match ringer Berlin Heidelberg:15-29, 2010.
https://www.cs.cmu.edu/%7Eafarhadi/papers/sentence.pdf
[2] ★★★ ★
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
[3] ★★★
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
[4] RNN视觉识别与标注
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
[5] 李飞飞及高徒Andrej Karpathy ★★★ ★★
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
[6] 李飞飞及高徒Andrej Karpathy ★★★ ★
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
[7] ★★★ ★
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
[8] ★★★ ★
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
[9] ★★★
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
[10] ★★★ ★★
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
3.5 机器翻译
本话题的部分里程碑论文列在 2.4 “RNN / 序列到序列模型”话题下。
[1] ★★★ ★
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
[2] ★★★
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
[3] ★★★ ★
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
[4] ★★
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
[5] ★★★ ★★
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
[6] 里程碑 ★★★ ★
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
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.
http://repository.supsi.ch/4550/1/koutnik2013gecco.pdf
[2] ★★★ ★★
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
[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).
http://arxiv.org/pdf/1509.06825
[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).
http://arxiv.org/pdf/1603.02199
[5] ★★★ ★
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
[6] ★★★ ★
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
[7] ★★★ ★
Gu, Shixiang, et al. “Deep Reinforcement Learning for Robotic Manipulation.” arXiv preprint arXiv:1610.00633 (2016).
https://arxiv.org/pdf/1610.00633
[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).
https://arxiv.org/pdf/1610.04286.pdf
[9] ★★★ ★
Mirowski, Piotr, et al. “Learning to navigate in complex environments.” arXiv preprint arXiv:1611.03673 (2016).
https://arxiv.org/pdf/1611.03673
3.7 艺术
[1] Google Deep Dream ★★★ ★
Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). “Inceptionism: Going Deeper into Neural Networks”. Google Research.
https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html
[2] 当前最为成功的艺术风格迁移方案，Prisma ★★★ ★★
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
http://arxiv.org/pdf/1508.06576
[3] iGAN ★★★ ★
Zhu, Jun-Yan, et al. “Generative Visual Manipulation on the Natural Image Manifold.” European Conference on Computer Vision. Springer International Publishing, 2016.
https://arxiv.org/pdf/1609.03552
[4] Neural Doodle ★★★ ★
Champandard, Alex J. “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.” arXiv preprint arXiv:1603.01768 (2016).
http://arxiv.org/pdf/1603.01768
[5] ★★★ ★
Zhang, Richard, Phillip Isola, and Alexei A. Efros. “Colorful Image Colorization.” arXiv preprint arXiv:1603.08511 (2016).
http://arxiv.org/pdf/1603.08511
[6] 超分辨率，李飞飞 ★★★ ★
Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. “Perceptual losses for real-time style transfer and super-resolution.” arXiv preprint arXiv:1603.08155 (2016).
https://arxiv.org/pdf/1603.08155.pdf
[7] ★★★ ★
Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. “A learned representation for artistic style.” arXiv preprint arXiv:1610.07629 (2016).
https://arxiv.org/pdf/1610.07629v1.pdf
[8] 基于空间位置、色彩信息与空间尺度的风格迁移 ★★★ ★
Gatys, Leon and Ecker, et al.”Controlling Perceptual Factors in Neural Style Transfer.” arXiv preprint arXiv:1611.07865 (2016).
https://arxiv.org/pdf/1611.07865.pdf
[9] 纹理生成与风格迁移 ★★★ ★
Ulyanov, Dmitry and Lebedev, Vadim, et al. “Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.” arXiv preprint arXiv:1603.03417(2016).
http://arxiv.org/abs/1603.03417
3.8 目标分割
[1] ★★★ ★★
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015.
https://arxiv.org/pdf/1411.4038v2.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.
https://arxiv.org/pdf/1606.00915v1.pdf
[3] ★★★ ★
Pinheiro, P.O., Collobert, R., Dollar, P. “Learning to segment object candidates.” In: NIPS. 2015.
https://arxiv.org/pdf/1506.06204v2.pdf
[4] ★★★
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
[5] ★★★
Dai, J., He, K., Sun, J. “Instance-sensitive Fully Convolutional Networks.” arXiv preprint arXiv:1603.08678 (2016).
https://arxiv.org/pdf/1603.08678v1.pdf
