深度学习相关论文

╰半橙微兮° 2022-05-25 01:08 172阅读 0赞

本博客用于记录自己平时收集的一些不错的深度学习论文，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，本博客将伴随着我的研究生涯长期更新，如有错误或者推荐文章烦请私信。本文为转载文章，原文地址为:https://blog.csdn.net/qq\_21190081/article/details/69564634

## **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.[ \[PDF\]][_PDF]**（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).[\[PDF\]][PDF]**（深度学习经典书籍）**
 *  Deep Learning Tutorial[\[PDF\]][PDF 1]（**李宏毅的深度学习综述PPT，适合入门）**
 *  D L. LISA Lab\[J\]. University of Montreal, 2014.[\[PDF\]][PDF 2]**（Theano配套的深度学习教程）**
 *  deeplearningbook-chinese [\[Github\]][Github]**（深度学习中文书，大家一起翻译的）**
 *  \-

## **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.[\[PDF\]][PDF 3]**（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.[\[PDF\]][PDF 4]**（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.[\[PDF\]][PDF 5]**（自编码器降维）**
 *  Ng A. Sparse autoencoder\[J\]. CS294A Lecture notes, 2011, 72(2011): 1-19.[\[PDF\]][PDF 6]**（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.[\[PDF\]][PDF 7]**（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.[\[PDF\]][PDF 8]**（AlexNet）**
 *  Simonyan, Karen, and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).[\[PDF\]][PDF 9]**（VGGNet）**
 *  Szegedy, Christian, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [\[PDF\]][PDF 10]**（GoogLeNet）**
 *  Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision\[J\]. Computer Science, 2015:2818-2826.[\[PDF\]][PDF 11]**（InceptionV3）**
 *  He, Kaiming, et al. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015).[\[PDF\]][PDF 12]**（ResNet）**
 *  Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions\[J\]. arXiv preprint arXiv:1610.02357, 2016.[\[PDF\]][PDF 13]**（Xception）**
 *  Huang G, Liu Z, Weinberger K Q, et al. Densely Connected Convolutional Networks\[J\]. 2016. [\[PDF\]][PDF 14] **(DenseNet, 2017 CVPR best paper)**
 *  Squeeze-and-Excitation Networks. [\[PDF\]][PDF 15] **(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.[\[PDF\]][PDF 16]**（Shufflenet）**
 *  Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules\[C\]//Advances in Neural Information Processing Systems. 2017: 3859-3869.[\[PDF\]][PDF 17]**（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.[\[PDF\]][PDF 18]**（Dropout）**
 *  Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift\[J\]. arXiv preprint arXiv:1502.03167, 2015.[\[PDF\]][PDF 19]**（Batch Normalization）**
 *  Lin M, Chen Q, Yan S. Network In Network\[J\]. Computer Science, 2014.[\[PDF\]][PDF 20]**（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. [\[PDF\]][PDF 21]**（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.[\[PDF\]][PDF 22]**（RNN和语language model结合较经典文章）**
 *  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.[\[PDF\]][PDF 23]**（RNN预测股价）**
 *  Hochreiter S, Schmidhuber J. Long short-term memory\[J\]. Neural computation, 1997, 9(8): 1735-1780.[\[PDF\]][PDF 24]**（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.[\[PDF\]][PDF 25]**（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.[\[PDF\]][PDF 26]**（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.[\[PDF\]][PDF 27]**（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.[\[PDF\]][PDF 28]**（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.[\[PDF\]][PDF 29]**（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.[\[PDF\]][PDF 30]**（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.[\[PDF\]][PDF 31]**（Attention model用于image caption的经典文章）**
 *  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.[\[PDF\]][PDF 32]**（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.[\[PDF\]][PDF 33]**（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.[\[PDF\]][PDF 34]**（GAN的提出，挖坑鼻祖）**
 *  Mirza M, Osindero S. Conditional generative adversarial nets\[J\]. arXiv preprint arXiv:1411.1784, 2014.[\[PDF\]][PDF 35]**（CGAN）**
 *  Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks\[J\]. arXiv preprint arXiv:1511.06434, 2015.[\[PDF\]][PDF 36]**（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.[\[PDF\]][PDF 37]**（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.[\[PDF\]][PDF 38]**（InfoGAN）**
 *  Arjovsky M, Chintala S, Bottou L. Wasserstein gan\[J\]. arXiv preprint arXiv:1701.07875, 2017.[\[PDF\]][PDF 39]**（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.[\[PDF\]][PDF 40]**（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.[\[PDF\]][PDF 41]**（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.[\[PDF\]][PDF 42]**（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.[\[PDF\]][PDF 43]**（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.[\[PDF\]][PDF 44]**（何凯明大神的SPPNet）**
 *  Girshick R. Fast r-cnn\[C\]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440-1448.[\[PDF\]][PDF 44]**（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.[\[PDF\]][PDF 45]**（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.[\[PDF\]][PDF 46]**（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.[\[PDF\]][PDF 47]**（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.[\[PDF\]][PDF 48]**（R-fcn）**
 *  Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection\[J\]. arXiv preprint arXiv:1708.02002, 2017.[\[PDF\]][PDF 49]**（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.[\[PDF\]][PDF 50]**（One shot learning）**
 *  Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks\[J\]. 2008:646-651.[\[PDF\]][PDF 51]**（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.[\[PDF\]][PDF 52]**（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.[\[PDF\]][PDF 53]**（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.[\[PDF\]][PDF 54]**（DeepLab）**
 *  Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network\[J\]. arXiv preprint arXiv:1612.01105, 2016.[\[PDF\]][PDF 55]**（PSPNet）**
 *  Yu F, Koltun V, Funkhouser T. Dilated residual networks\[J\]. arXiv preprint arXiv:1705.09914, 2017.[\[PDF\]][PDF 56]
 *  He K, Gkioxari G, Dollár P, et al. Mask R-CNN\[J\]. arXiv preprint arXiv:1703.06870, 2017.[\[PDF\]][PDF 57]**（何凯明大神的MASK r-cnn，膜）**
 *  Hu R, Dollár P, He K, et al. Learning to Segment Every Thing\[J\]. arXiv preprint arXiv:1711.10370, 2017.[\[PDF\]][PDF 58]**（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.[\[PDF\]][PDF 59]**（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.[\[PDF\]][PDF 60]**（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.[\[PDF\]][PDF 61]**（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.[\[PDF\]][PDF 62]**（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.[\[PDF\]][PDF 63]**（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.[\[PDF\]][PDF 64]**(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. [\[PDF\]][PDF 65]**（AlignedReid，首次超越人类）**
 *  [Liang Zheng的个人主页][Liang Zheng]（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）

[_PDF]: http://pages.cs.wisc.edu/~dyer/cs540/handouts/deep-learning-nature2015.pdf
[PDF]: https://github.com/HFTrader/DeepLearningBook/raw/master/DeepLearningBook.pdf
[PDF 1]: http://pan.baidu.com/s/1pKH3PGn
[PDF 2]: http://pan.baidu.com/s/1bZ5nSa
[Github]: https://github.com/exacity/deeplearningbook-chinese
[PDF 3]: https://pdfs.semanticscholar.org/4d3f/050801bd76ef10855ce115c31b301a83b405.pdf
[PDF 4]: http://www.mitpressjournals.org/doi/pdfplus/10.1162/neco.2006.18.7.1527
[PDF 5]: https://pdfs.semanticscholar.org/7d76/b71b700846901ac4ac119403aa737a285e36.pdf
[PDF 6]: https://pdfs.semanticscholar.org/eb2f/e260af00818907fe82024203d8a5a1386777.pdf
[PDF 7]: http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf
[PDF 8]: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
[PDF 9]: https://arxiv.org/pdf/1409.1556.pdf
[PDF 10]: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
[PDF 11]: http://xueshu.baidu.com/s?wd=paperuri:%286cb3924c8d2bc09bc91ccff3f8be0f55%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http://arxiv.org/pdf/1512.00567v3.pdf&ie=utf-8&sc_us=6968646265670184715
[PDF 12]: https://arxiv.org/pdf/1512.03385.pdf
[PDF 13]: https://arxiv.org/pdf/1610.02357
[PDF 14]: https://arxiv.org/pdf/1608.06993.pdf
[PDF 15]: https://arxiv.org/pdf/1709.01507.pdf
[PDF 16]: https://arxiv.org/pdf/1707.01083
[PDF 17]: http://papers.nips.cc/paper/6975-dynamic-routing-between-capsules.pdf
[PDF 18]: http://www.jmlr.org/papers/volume15/srivastava14a.old/source/srivastava14a.pdf
[PDF 19]: https://arxiv.org/pdf/1502.03167
[PDF 20]: http://xueshu.baidu.com/s?wd=paperuri:%28b141af8f73398aaf072b958f50807e1b%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http://arxiv.org/pdf/1312.4400&ie=utf-8&sc_us=13783729042765531986
[PDF 21]: https://arxiv.org/abs/1706.02677
[PDF 22]: http://www.fit.vutbr.cz/research/groups/speech/servite/2010/rnnlm_mikolov.pdf
[PDF 23]: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5726532
[PDF 24]: http://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf
[PDF 25]: https://g.zmirrordemo.com/extdomains/http://193.6.4.39/~czap/letoltes/IS14/IS2014/PDF/AUTHOR/IS141304.PDF
[PDF 26]: https://arxiv.org/pdf/1412.3555
[PDF 27]: https://arxiv.org/pdf/1508.02096
[PDF 28]: https://arxiv.org/pdf/1508.01991
[PDF 29]: https://arxiv.org/pdf/1409.0473
[PDF 30]: http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf
[PDF 31]: http://www.jmlr.org/proceedings/papers/v37/xuc15.pdf
[PDF 32]: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.pdf
[PDF 33]: https://arxiv.org/pdf/1502.04623
[PDF 34]: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
[PDF 35]: https://arxiv.org/pdf/1411.1784
[PDF 36]: https://arxiv.org/pdf/1511.06434.pdf%C3%AF%C2%BC%E2%80%B0
[PDF 37]: http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf
[PDF 38]: http://papers.nips.cc/paper/6398-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf
[PDF 39]: https://arxiv.org/pdf/1701.07875
[PDF 40]: https://arxiv.org/pdf/1704.02510
[PDF 41]: https://arxiv.org/pdf/1611.07004
[PDF 42]: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf
[PDF 43]: http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf
[PDF 44]: http://arxiv.org/pdf/1406.4729
[PDF 45]: http://papers.nips.cc/paper/5638-analysis-of-variational-bayesian-latent-dirichlet-allocation-weaker-sparsity-than-map.pdf
[PDF 46]: http://homes.cs.washington.edu/~ali/papers/YOLO.pdf
[PDF 47]: http://arxiv.org/pdf/1512.02325
[PDF 48]: https://arxiv.org/abs/1605.06409
[PDF 49]: https://arxiv.org/pdf/1708.02002
[PDF 50]: http://www.cs.huji.ac.il/~daphna/course/student%20lectures/cobi%20cario.pdf
[PDF 51]: http://www.aaai.org/Papers/AAAI/2008/AAAI08-103.pdf
[PDF 52]: http://papers.nips.cc/paper/3650-zero-shot-learning-with-semantic-output-codes.pdf
[PDF 53]: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf
[PDF 54]: https://arxiv.org/pdf/1606.00915
[PDF 55]: https://arxiv.org/pdf/1612.01105
[PDF 56]: https://arxiv.org/pdf/1705.09914
[PDF 57]: https://arxiv.org/pdf/1703.06870
[PDF 58]: https://arxiv.org/pdf/1711.10370
[PDF 59]: https://arxiv.org/pdf/1407.4979
[PDF 60]: https://arxiv.org/pdf/1512.03622.pdf
[PDF 61]: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cheng_Person_Re-Identification_by_CVPR_2016_paper.pdf
[PDF 62]: https://arxiv.org/pdf/1703.07737
[PDF 63]: https://arxiv.org/pdf/1704.01719.pdf
[PDF 64]: http://openaccess.thecvf.com/content_ICCV_2017/papers/Zheng_Unlabeled_Samples_Generated_ICCV_2017_paper.pdf
[PDF 65]: https://arxiv.org/pdf/1711.08184
[Liang Zheng]: http://www.liangzheng.org/