本部落格用於記錄自己平時收集的一些不錯的深度學習論文，近9成的文章都是引用量3位數以上的論文，剩下少部分來自個人喜好，本部落格將伴隨著我的研究生涯長期更新，如有錯誤或者推薦文章煩請私信。

深度學習書籍和入門資源

• LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. [PDF]（深度學習最權威的綜述）
• Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. Deep learning. An MIT Press book. (2015).
  [PDF]（深度學習經典書籍）
• Deep Learning Tutorial[PDF]（李巨集毅的深度學習綜述PPT，適合入門）
• D L. LISA Lab[J]. University of Montreal, 2014.[PDF]（Theano配套的深度學習教程）
• deeplearningbook-chinese [Github]（深度學習中文書，大家一起翻譯的）
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早期的深度學習

• Hecht-Nielsen R. Theory of the backpropagation neural network[J]. Neural Networks, 1988, 1(Supplement-1): 445-448.
  [PDF]（BP神經網路）
• Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets.[J]. Neural Computation, 2006, 18(7):1527-1554.[PDF]（深度學習的開端DBN）
• Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks.[J]. Science, 2006, 313(5786):504-7.[PDF]（自編碼器降維）
• Ng A. Sparse autoencoder[J]. CS294A Lecture notes, 2011, 72(2011): 1-19.
  [PDF]（稀疏自編碼器）
• 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.[PDF]（堆疊自編碼器，SAE）

深度學習的爆發:ImageNet挑戰賽

• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012.[PDF]（AlexNet）
• Simonyan, Karen, and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).[PDF]（VGGNet）
• Szegedy, Christian, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [PDF]（GoogLeNet）
• Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision[J]. Computer Science, 2015:2818-2826.[PDF]（InceptionV3）
• He, Kaiming, et al. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015).[PDF]（ResNet）
• Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions[J]. arXiv preprint arXiv:1610.02357, 2016.[PDF]（Xception）
• Huang G, Liu Z, Weinberger K Q, et al. Densely Connected Convolutional Networks[J]. 2016. [PDF] (DenseNet, 2017 CVPR best paper)
• Squeeze-and-Excitation Networks. [PDF] (SeNet, 2017 ImageNet 冠軍)
• 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.[PDF]（Shufflenet）
• Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems. 2017: 3859-3869.[PDF]（Hinton, capsules）

煉丹技巧

• 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.[PDF]（Dropout）
• Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.[PDF]（Batch Normalization）
• Lin M, Chen Q, Yan S. Network In Network[J]. Computer Science, 2014.[PDF]（Global average pooling的靈感來源）
• Goyal, Priya, Dollár, Piotr, Girshick, Ross, et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour[J]. 2017. [PDF]（Facebook實驗室的成果，解決了工程上網路batchsize特大時效能下降的問題）

遞迴神經網路

• Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model[C]//Interspeech. 2010, 2: 3.[PDF]（RNN和語language model結合較經典文章）
• 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.[PDF]（RNN預測股價）
• Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.[PDF]（LSTM的數學原理）
• 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.[PDF]（LSTM進行語音識別）
• 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.[PDF]（GRU網路）
• 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.[PDF]（LSTM在詞向量中的應用）
• Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.[PDF]（Bi-LSTM在序列標註中的應用）

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.[PDF]（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.[PDF]（Attention model和視覺結合）
• 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.[PDF]（Attention model用於image caption的經典文章）
• 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.[PDF]（Attention model 用於OCR）
• Gregor K, Danihelka I, Graves A, et al. DRAW: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623, 2015.[PDF]（DRAM，結合Attention model的影象生成）

生成對抗網路

• Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680.[PDF]（GAN的提出，挖坑鼻祖）
• Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.[PDF]（CGAN）
• Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.[PDF]（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.[PDF]（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.[PDF]（InfoGAN）
• Arjovsky M, Chintala S, Bottou L. Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017.[PDF]（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.[PDF]（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.[PDF]（pix2pix）

目標檢測

• Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection[C]//Advances in Neural Information Processing Systems. 2013: 2553-2561.[PDF]（深度學習早期的物體檢測）
• 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）
• 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.[PDF]（何凱明大神的SPPNet）
• Girshick R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440-1448.[PDF]（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.[PDF]（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.[PDF]（實時目標檢測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.[PDF]（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.[PDF]（R-fcn）
• Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. arXiv preprint arXiv:1708.02002, 2017.[PDF]（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.[PDF]（One shot learning）
• Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks[J]. 2008:646-651.[PDF]（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.[PDF]（Zero shot learning比較經典的應用）

影象分割

• 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.[PDF]（有點老但是非常經典的影象語義分割論文，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.[PDF]（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.[PDF]
• He K, Gkioxari G, Dollár P, et al. Mask R-CNN[J]. arXiv preprint arXiv:1703.06870, 2017.[PDF]（何凱明大神的MASK r-cnn，膜）
• Hu R, Dollár P, He K, et al. Learning to Segment Every Thing[J]. arXiv preprint arXiv:1711.10370, 2017.[PDF]（Mask Rcnn增強版） 
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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.[PDF]（較早的一篇基於CNN的度量學習的Re-ID，現在來看網路已經很簡單了）
• 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.[PDF]（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.[PDF]（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.[PDF]（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.[PDF]（四元組）
• 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.[PDF](用GAN造圖做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. [PDF]（AlignedReid，首次超越人類）
• Liang Zheng的個人主頁（在這個領域提供了大量論文，常用的資料集和程式碼都可以在主頁中找到）

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