{"action":"opened","issue":{"url":"https://api.github.com/repos/number9473/datanmachlearn/issues/12","repository_url":"https://api.github.com/repos/number9473/datanmachlearn","labels_url":"https://api.github.com/repos/number9473/datanmachlearn/issues/12/labels{/name}","comments_url":"https://api.github.com/repos/number9473/datanmachlearn/issues/12/comments","events_url":"https://api.github.com/repos/number9473/datanmachlearn/issues/12/events","html_url":"https://github.com/number9473/datanmachlearn/issues/12","id":162310080,"number":12,"title":"全球30篇最热门的计算机视觉和深度学习论文","user":{"login":"joyhuang9473","id":5389311,"avatar_url":"https://avatars.githubusercontent.com/u/5389311?v=3","gravatar_id":"","url":"https://api.github.com/users/joyhuang9473","html_url":"https://github.com/joyhuang9473","followers_url":"https://api.github.com/users/joyhuang9473/followers","following_url":"https://api.github.com/users/joyhuang9473/following{/other_user}","gists_url":"https://api.github.com/users/joyhuang9473/gists{/gist_id}","starred_url":"https://api.github.com/users/joyhuang9473/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/joyhuang9473/subscriptions","organizations_url":"https://api.github.com/users/joyhuang9473/orgs","repos_url":"https://api.github.com/users/joyhuang9473/repos","events_url":"https://api.github.com/users/joyhuang9473/events{/privacy}","received_events_url":"https://api.github.com/users/joyhuang9473/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-06-26T03:10:11Z","updated_at":"2016-06-26T03:10:11Z","closed_at":null,"body":"# 全球30篇最热门的计算机视觉和深度学习论文 #\r\n\r\n- Author: [http://www.afenxi.com](http://www.afenxi.com)\r\n- Origin: [http://www.afenxi.com/post/19793](http://www.afenxi.com/post/19793)\r\n- Related:\r\n\r\n> 1.引用次数：5518\r\n标题：深度卷积神经网络分类(Imagenet classification with deep convolutional neural networks)\r\nA Krizhevsky, I Sutskever, GE Hinton, 2012\r\n[论文链接地址](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf?spm=5176.100239.blogcont55892.5.pm8zm1&file=4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\r\n\r\n> 2.引用次数：1868\r\n标题：咖啡：快速卷积结构特征嵌入(Caffe: Convolutional architecture for fast feature embedding)\r\nY Jia, E Shelhamer, J Donahue, S Karayev…, 2014\r\n[论文链接地址](http://dl.acm.org/citation.cfm?spm=5176.100239.blogcont55892.6.pm8zm1&id=2654889)\r\n\r\n> 3.引用次数：1681\r\n标题：反向传播算法在手写体邮政编码识别中的应用(Backpropagation applied to handwritten zip code recognition)\r\nY LeCun, B Boser, JS Denker, D Henderson…, 1989\r\n[论文链接地址](http://www.mitpressjournals.org/doi/abs/10.1162/neco.1989.1.4.541?spm=5176.100239.blogcont55892.7.pm8zm1#.V2fy_-wQgg8)\r\n\r\n> 4.引用次数：1516\r\n标题：实现精确对象和语义段检测的富特征层次结构(Rich feature hierarchies for accurate object detection and semantic segment)\r\nR Girshick, J Donahue, T Darrell…, 2014\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.8.pm8zm1&file=Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf)\r\n\r\n> 5.引用次数：1405\r\n标题： 进行大型图像识别的深层网络(Very deep convolutional networks for large-scale image recognition)\r\nK Simonyan, A Zisserman, 2014\r\n[论文链接地址](http://arxiv.org/abs/1409.1556?spm=5176.100239.blogcont55892.9.pm8zm1&file=1409.1556)\r\n\r\n> 6.引用次数：1169\r\n标题：通过共适应的特征探测器来改进神经网络(Improving neural networks by preventing co-adaptation of feature detectors)\r\nGE Hinton, N Srivastava, A Krizhevsky…, 2012\r\n[论文链接地址](http://arxiv.org/abs/1207.0580?spm=5176.100239.blogcont55892.10.pm8zm1&file=1207.0580)\r\n\r\n> 7.引用次数：1160\r\n标题：深入了解卷积(Going deeper with convolutions)\r\nC Szegedy, W Liu, Y Jia, P Sermanet…, 2015\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf?spm=5176.100239.blogcont55892.11.pm8zm1&file=Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf)\r\n\r\n> 8.引用次数：977\r\n标题：反向传播网络的手写数字识别(Handwritten digit recognition with a back-propagation network)\r\nBB Le Cun, JS Denker, D Henderson…, 1990\r\n[论文链接地址](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=CB3ADCB9B99C266A5ECA1B7FA92D3AC7?spm=5176.100239.blogcont55892.12.pm8zm1&doi=10.1.1.32.5076&rep=rep1&type=pdf)\r\n\r\n> 9.引用次数：907\r\n标题：观察和理解卷积网络(Visualizing and understanding convolutional networks)\r\nMD Zeiler, R Fergus, 2014\r\n[论文链接地址](http://link.springer.com/chapter/10.1007/978-3-319-10590-1_53?spm=5176.100239.blogcont55892.13.pm8zm1)\r\n\r\n> 10.引用次数：839\r\n标题： 降阶—以简单的方法来防止神经网络拟合(Dropout: a simple way to prevent neural networks from overfitting)\r\nN Srivastava, GE Hinton, A Krizhevsky…, 2014\r\n[论文链接地址](http://www.jmlr.org/papers/volume15/srivastava14a.old/source/srivastava14a.pdf?spm=5176.100239.blogcont55892.14.pm8zm1&file=srivastava14a.pdf)\r\n\r\n> 11.引用次数：839\r\n标题：Overfeat框架:使用卷积网络进行综合识别、 定位和检测(Overfeat: Integrated recognition, localization and detection using convolutional networks)\r\nP Sermanet, D Eigen, X Zhang, M Mathieu…, 2013\r\n[论文链接地址](http://arxiv.org/abs/1312.6229?spm=5176.100239.blogcont55892.15.pm8zm1&file=1312.6229)\r\n\r\n> 12.引用次数：818\r\n标题：从微图像里学习多层次特征(Learning multiple layers of features from tiny images)\r\nA Krizhevsky, G Hinton, 2009\r\n[论文链接地址](http://citeseerx.ist.psu.edu/viewdoc/download?spm=5176.100239.blogcont55892.16.pm8zm1&doi=10.1.1.222.9220&rep=rep1&type=pdf)\r\n\r\n> 13.引用次数：718\r\n标题：无咖啡因:一个用于通用图像识别的深积激活特征(DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition)\r\nJ Donahue, Y Jia, O Vinyals, J Hoffman, N Zhang…, 2014\r\n[论文链接地址](http://www.jmlr.org/proceedings/papers/v32/donahue14.pdf?spm=5176.100239.blogcont55892.17.pm8zm1&file=donahue14.pdf)\r\n\r\n> 14.引用次数：691\r\n标题： 深度人脸:拉近与真人之间的面部识别差距(Deepface: Closing the gap to human-level performance in face verification)\r\nY Taigman, M Yang, MA Ranzato…, 2014\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.18.pm8zm1&file=Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf)\r\n\r\n> 15.引用次数：679\r\n标题：深入玻耳兹曼机 (Deep Boltzmann Machines)\r\nR Salakhutdinov, GE Hinton, 2009\r\n[论文链接地址](http://www.jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf?spm=5176.100239.blogcont55892.19.pm8zm1&file=salakhutdinov09a.pdf)\r\n\r\n> 16.引用次数：670\r\n标题：用于图像、 语音和时间序列的卷积网络(Convolutional networks for images, speech, and time series)\r\nY LeCun, Y Bengio, 1995\r\n[论文链接地址](https://www.researchgate.net/publication/2453996_Convolutional_Networks_for_Images_Speech_and_Time-Series?spm=5176.100239.blogcont55892.20.pm8zm1)\r\n\r\n> 17.引用次数：570\r\n标题：现成的 CNN 功能: 令人震惊的基线识别(CNN features off-the-shelf: an astounding baseline for recognition)\r\nA Sharif Razavian, H Azizpour, J Sullivan…, 2014\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W15/papers/Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.21.pm8zm1&file=Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf)\r\n\r\n> 18.引用次数：549\r\n标题：学习场景标识中的分层结构特征 (Learning hierarchical features for scene labeling)\r\nC Farabet, C Couprie, L Najman…, 2013\r\n[论文链接地址](http://ieeexplore.ieee.org/xpl/login.jsp?spm=5176.100239.blogcont55892.22.pm8zm1&tp=&arnumber=6338939&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6338939)\r\n\r\n> 19.引用次数：510\r\n标题：完全卷积网络的语义分割(Fully convolutional networks for semantic segmentation)\r\nJ Long, E Shelhamer, T Darrell, 2015\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf?spm=5176.100239.blogcont55892.23.pm8zm1&file=Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)\r\n\r\n> 20.引用次数：469\r\n标题： 最大输出网络(Maxout networks)\r\nIJ Goodfellow, D Warde-Farley, M Mirza, AC Courville…, 2013\r\n[论文链接地址](http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf?spm=5176.100239.blogcont55892.24.pm8zm1&file=goodfellow13.pdf)\r\n\r\n> 21.引用次数：453\r\n标题：精绝的细节:深度钻研卷积网(Return of the devil in the details: Delving deep into convolutional nets)\r\nK Chatfield, K Simonyan, A Vedaldi…, 2014\r\n[论文链接地址](http://arxiv.org/abs/1405.3531?spm=5176.100239.blogcont55892.25.pm8zm1&file=1405.3531)\r\n\r\n> 22.引用次数：445\r\n标题：使用卷积神经网络进行大型视频分类 (Large-scale video classification with convolutional neural networks)\r\nA Karpathy, G Toderici, S Shetty, T Leung…, 2014\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.26.pm8zm1&file=Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf)\r\n\r\n> 23.引用次数：347\r\n标题：深度视觉语义路线生成图像描述(Deep visual-semantic alignments for generating image descriptions)\r\nA Karpathy, L Fei-Fei, 2015\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf?spm=5176.100239.blogcont55892.27.pm8zm1&file=Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf)\r\n\r\n> 24.引用次数：342\r\n标题：深入学习整流器:超越人类水平的图像分类(Delving deep into rectifiers: Surpassing human-level performance on imagenet classification)\r\nK He, X Zhang, S Ren, J Sun, 2015\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf?spm=5176.100239.blogcont55892.28.pm8zm1&file=He_Delving_Deep_into_ICCV_2015_paper.pdf)\r\n\r\n> 25.引用次数：334\r\n标题：使用卷积神经网络来学习和转移中级图像显示 (Learning and transferring mid-level image representations using convolutional neural networks)\r\nM Oquab, L Bottou, I Laptev, J Sivic, 2014\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.29.pm8zm1&file=Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf)\r\n\r\n> 26.引用次数：333\r\n标题：卷积网络及其在视觉中的应用(Convolutional networks and applications in vision)\r\nY LeCun, K Kavukcuoglu, C Farabet, 2010\r\n[论文链接地址](http://research2.fit.edu/ice/sites/default/files/Convolutional%20networks%20and%20applications%20in%20vision_0.pdf?spm=5176.100239.blogcont55892.30.pm8zm1&file=Convolutional%20networks%20and%20applications%20in%20vision_0.pdf)\r\n\r\n> 27.引用次数：332\r\n标题：使用场景数据库进行深度场景识别(Learning deep features for scene recognition using places database)\r\nB Zhou, A Lapedriza, J Xiao, A Torralba…,2014\r\n[论文链接地址](http://papers.nips.cc/paper/5349-learning-deep-features-for-scene-recognition-using-places-database.pdf?spm=5176.100239.blogcont55892.31.pm8zm1&file=5349-learning-deep-features-for-scene-recognition-using-places-database.pdf)\r\n\r\n> 28.引用次数：299\r\n标题：使用深积网络中的空间金字塔池进行视觉识别 (Spatial pyramid pooling in deep convolutional networks for visual recognition)\r\nK He, X Zhang, S Ren, J Sun, 2014\r\n[论文链接地址](http://link.springer.com/chapter/10.1007/978-3-319-10578-9_23?spm=5176.100239.blogcont55892.32.pm8zm1)\r\n\r\n> 29.引用次数：268\r\n标题：使用长期递归卷积网络进行视觉识别和描述(Long-term recurrent convolutional networks for visual recognition and description)\r\nJ Donahue, L Anne Hendricks…, 2015\r\n[论文链接地址](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Donahue_Long-Term_Recurrent_Convolutional_2015_CVPR_paper.pdf?spm=5176.100239.blogcont55892.33.pm8zm1&file=Donahue_Long-Term_Recurrent_Convolutional_2015_CVPR_paper.pdf)\r\n\r\n> 30.引用次数：261\r\n标题：使用二流卷积网络进行动作视频识别(Two-stream convolutional networks for action recognition in videos)\r\nK Simonyan, A Zisserman, 2014\r\n[论文链接地址](http://papers.nips.cc/paper/5353-two-stream-convolutional-networks-for-action-recognition-in-videos.pdf?spm=5176.100239.blogcont55892.34.pm8zm1&file=5353-two-stream-convolutional-networks-for-action-recognition-in-videos.pdf)\r\n"}}
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