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【更新於12.29】深度學習論文彙總

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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