CaffeCN推荐阅读论文列表

更新
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CaffeCN推荐阅读论文列表(持续更新中 20160131)

1.理论
1.1 综述
  • Lecun Y, Bengio Y, Hinton G. Deep learning.[J]. Nature, 2015, 521(7553):436-44.
  • Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117.

1.2 数学基础
  • K. B. Petersen and M. S. Pedersen, “The matrix cookbook,” nov 2012, Version 20121115.

 
1.3 收敛理论
  • Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//International conference on artificial intelligence and statistics. 2010: 249-256.
  • Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.
  • Neyshabur B, Salakhutdinov R R, Srebro N. Path-sgd: Path-normalized optimization in deep neural networks[C]//Advances in Neural Information Processing Systems. 2015: 2413-2421.

 
2. 模型
2.1 CNN
  • Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.
  • Szegedy C, Liu W, Jia Y, et al. Going Deeper With Convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1-9.
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
  • Srivastava R K, Greff K, Schmidhuber J. Highway Networks[J]. arXiv preprint arXiv:1505.00387, 2015.


2.2 RNN
  • Graves A. Long Short-Term Memory[J]. Neural Computation, 1997, 9(8):1735-1780.
  • Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(s 5–6):602-610.
  • Chung J, Gulcehre C, Cho K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[J]. Eprint Arxiv, 2014.


3.应用
 
3.1 图像分类
  • Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.
  • He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[J]. arXiv preprint arXiv:1512.03385, 2015.
  • Kontschieder P, Fiterau M, Criminisi A, et al. Deep Neural Decision Forests[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1467-1475.(ICCV2015 Marr Prize)
  • Joint Embeddings of Shapes and Images via CNN Image Purification ACM Transactions on Graphics (Proceeding of SIGGRAPH Asia 2015)

 
3.2 人脸识别
  • Taigman Y, Yang M, Ranzato M, Wolf L. Deepface: Closing the gap to human-level performance in face verification. In: Computer Vision and Pattern Recognition (CVPR). 2014
  •  Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition (CVPR). 2014, 1891–1898
  •  Sun Y, Chen Y, Wang X, Tang X. Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems (NIPS). 2014, 1988–1996
  • Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv:1412.1265, 2014
  • Yi D, Lei Z, Liao S, Li S Z. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014
  •  Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering. arXiv preprint arXiv:1503.03832, 2015


3.3 目标检测
  • 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.
  • Girshick R. Fast R-CNN[J]. arXiv preprint arXiv:1504.08083, 2015.(ICCV2015)
  • Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014: 580-587.
  • Hosang J, Benenson R, Dollár P, et al. What makes for effective detection proposals[J]. arXiv preprint arXiv:1502.05082, 2015.(TPAMI2015)
  • Yoo D, Park S, Lee J Y, et al. AttentionNet: Aggregating Weak Directions for Accurate Object Detection[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 2659-2667.


3.4 OCR
  • Graves A, Schmidhuber J. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.[J]. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. - ResearchGate, 2008:545-552.


3.5 图像描述
  • Donahue J, Hendricks L A, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]// Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015.


 3.6 动作识别
  • Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos[C]//Advances in Neural Information Processing Systems. 2014: 568-576.



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说明:本列表由CaffeCN社区(caffecn.cn)答疑组共同整理,仅提供给CaffeCN社区使用,如需转载须注明转载来源。
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11 个评论

什么时候更新呢,好期待
2016.01.20 更新目标检测和RNN部分
赞赞赞,作为代码狗不用花时间去筛选论文了
如果能关注就好了,更新就有通知多好
期待持续更新
新人一枚,赞一个
好久没有更新啦
如果有原论文版链接就更好了
赞赞赞,期待更新
坐等新的更新
CaffeCN社区开辟了论文主题站,定期更新各领域最新的重要论文,http://paper.caffecn.cn/
欢迎各位到论文主题站推荐论文,如果您对某篇论文有疑惑,也欢迎您在各论文的主题下提问和讨论。

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