AI 科技评论按：本文作者罗浩，AI 科技评论授权转载。

本文用于记录自己平时收集的一些不错的往年（截止至 2017 / 12 / 29）深度学习论文，近9成的文章都是引用量3位数以上的论文，剩下少部分来自个人喜好，将伴随着我的研究生涯长期更新。

深度学习书籍和入门资源

• LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444（深度学习最权威的综述）

• Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. Deep learning. An MIT Press book. (2015)（深度学习经典书籍）

• Deep Learning Tutorial（李宏毅的深度学习综述PPT，适合入门）

• D L. LISA Lab[J]. University of Montreal, 2014.（Theano配套的深度学习教程）

• deeplearningbook-chinese（深度学习中文书，大家一起翻译的）

*在 AI 科技评论公众号回复“ 元旦资源 ”获取李宏毅深度学习综述PPT / Theano配套的深度学习教程 / 深度学习中文书。

早期的深度学习

• Hecht-Nielsen R. Theory of the backpropagation neural network[J]. Neural Networks, 1988, 1(Supplement-1): 445-448.

  （BP神经网络）

• Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets.[J]. Neural Computation, 2006, 18(7):1527-1554.（深度学习的开端DBN）

• Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks.[J]. Science, 2006, 313(5786):504-7.（自编码器降维）

• Ng A. Sparse autoencoder[J]. CS294A Lecture notes, 2011, 72(2011): 1-19.（稀疏自编码器）

• Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(Dec): 3371-3408.（堆叠自编码器，SAE）

深度学习的爆发:ImageNet挑战赛

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

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

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

• Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision[J]. Computer Science, 2015:2818-2826.（InceptionV3）

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

• Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions[J]. arXiv preprint arXiv:1610.02357, 2016.（Xception）

• Huang G, Liu Z, Weinberger K Q, et al. Densely Connected Convolutional Networks[J]. 2016. (DenseNet, 2017 CVPR best paper)

• Squeeze-and-Excitation Networks. (SeNet, 2017 ImageNet 冠军)

• Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[J]. arXiv preprint arXiv:1707.01083, 2017.（Shufflenet）

• Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems. 2017: 3859-3869.（Hinton, capsules）

炼丹技巧

• Srivastava N, Hinton G E, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.（Dropout）

• Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.（Batch Normalization）

• Lin M, Chen Q, Yan S. Network In Network[J]. Computer Science, 2014.（Global average pooling的灵感来源）

• Goyal, Priya, Dollár, Piotr, Girshick, Ross, et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour[J]. 2017. （Facebook实验室的成果，解决了工程上网络batchsize特大时性能下降的问题）

递归神经网络

• Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model[C]//Interspeech. 2010, 2: 3.

• Kamijo K, Tanigawa T. Stock price pattern recognition-a recurrent neural network approach[C]//Neural Networks, 1990., 1990 IJCNN International Joint Conference on. IEEE, 1990: 215-221.（RNN预测股价）

• Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.（LSTM的数学原理）

• Sak H, Senior A W, Beaufays F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling[C]//Interspeech. 2014: 338-342.（LSTM进行语音识别）

• Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint arXiv:1412.3555, 2014.（GRU网络）

• Ling W, Luís T, Marujo L, et al. Finding function in form: Compositional character models for open vocabulary word representation[J]. arXiv preprint arXiv:1508.02096, 2015.（LSTM在词向量中的应用）

• Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.（Bi-LSTM在序列标注中的应用）

注意力模型

• Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014.（Attention model的提出）

• Mnih V, Heess N, Graves A. Recurrent models of visual attention[C]//Advances in neural information processing systems. 2014: 2204-2212.（Attention model和视觉结合）

• Xu K, Ba J, Kiros R, et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention[C]//ICML. 2015, 14: 77-81.

• Lee C Y, Osindero S. Recursive Recurrent Nets with Attention Modeling for OCR in the Wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2231-2239.（Attention model 用于OCR）

• Gregor K, Danihelka I, Graves A, et al. DRAW: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623, 2015.（DRAM，结合Attention model的图像生成）

生成对抗网络

• Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680.（GAN的提出，挖坑鼻祖）

• Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.（CGAN）

• Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.（DCGAN）

• Denton E L, Chintala S, Fergus R. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks[C]//Advances in neural information processing systems. 2015: 1486-1494.（LAPGAN）

• Chen X, Duan Y, Houthooft R, et al. Infogan: Interpretable representation learning by information maximizing generative adversarial nets[C]//Advances in Neural Information Processing Systems. 2016: 2172-2180.（InfoGAN）

• Arjovsky M, Chintala S, Bottou L. Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017.（WGAN）

• Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[J]. arXiv preprint arXiv:1703.10593, 2017.（CycleGAN）

• Yi Z, Zhang H, Gong P T. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation[J]. arXiv preprint arXiv:1704.02510, 2017.（DualGAN）

• Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[J]. arXiv preprint arXiv:1611.07004, 2016.（pix2pix）

目标检测

• Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection[C]//Advances in Neural Information Processing Systems. 2013: 2553-2561.（深度学习早期的物体检测）

• 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.（RCNN）

• He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//European Conference on Computer Vision. Springer International Publishing, 2014: 346-361.（何恺明大神的SPPNet）

• Girshick R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440-1448.（速度更快的Fast R-cnn）

• Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 2015: 91-99.（速度更更快的Faster r-cnn）

• Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 779-788.（实时目标检测YOLO）

• Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Springer International Publishing, 2016: 21-37.（SSD）

• Li Y, He K, Sun J. R-fcn: Object detection via region-based fully convolutional networks[C]//Advances in Neural Information Processing Systems. 2016: 379-387.

  （R-fcn）

• Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. arXiv preprint arXiv:1708.02002, 2017.（Focal loss）

One/Zero shot learning

• Fei-Fei L, Fergus R, Perona P. One-shot learning of object categories[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(4): 594-611.（One shot learning）

• Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks[J]. 2008:646-651.（Zero shot learning的提出）

• Palatucci M, Pomerleau D, Hinton G E, et al. Zero-shot learning with semantic output codes[C]//Advances in neural information processing systems. 2009: 1410-1418.（Zero shot learning比较经典的应用）

图像分割

• Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440.（有点老但是非常经典的图像语义分割论文，CVPR2015）

• Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. arXiv preprint arXiv:1606.00915, 2016.（DeepLab）

• Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[J]. arXiv preprint arXiv:1612.01105, 2016.[PDF]（PSPNet）

• Yu F, Koltun V, Funkhouser T. Dilated residual networks[J]. arXiv preprint arXiv:1705.09914, 2017.

• He K, Gkioxari G, Dollár P, et al. Mask R-CNN[J]. arXiv preprint arXiv:1703.06870, 2017.[PDF]（何恺明大神的MASK r-cnn，膜）

• Hu R, Dollár P, He K, et al. Learning to Segment Every Thing[J]. arXiv preprint arXiv:1711.10370, 2017.（Mask Rcnn增强版）

Person Re-ID

• Yi D, Lei Z, Liao S, et al. Deep metric learning for person re-identification[C]//Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, 2014: 34-39.（较早的一篇基于CNN的度量学习的Re-ID，现在来看网络已经很简单了）

• Ding S, Lin L, Wang G, et al. Deep feature learning with relative distance comparison for person re-identification[J]. Pattern Recognition, 2015, 48(10): 2993-3003.（triplet loss）

• Cheng D, Gong Y, Zhou S, et al. Person re-identification by multi-channel parts-based cnn with improved triplet loss function[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1335-1344.（improved triplet loss）

• Hermans A, Beyer L, Leibe B. In Defense of the Triplet Loss for Person Re-Identification[J]. arXiv preprint arXiv:1703.07737, 2017.（Triplet loss with hard mining sample）

• Chen W, Chen X, Zhang J, et al. Beyond triplet loss: a deep quadruplet network for person re-identification[J]. arXiv preprint arXiv:1704.01719, 2017.（四元组）

• Zheng Z, Zheng L, Yang Y. Unlabeled samples generated by gan improve the person re-identification baseline in vitro[J]. arXiv preprint arXiv:1701.07717, 2017. (用GAN造图做ReID第一篇)

• Zhang X, Luo H, Fan X, et al. AlignedReID: Surpassing Human-Level Performance in Person Re-Identification[J]. arXiv preprint arXiv:1711.08184, 2017. （AlignedReid，首次超越人类）

• Liang Zheng的个人主页（在这个领域提供了大量论文，常用的数据集和代码都可以在主页中找到）

（完）

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