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@@ -6,6 +6,17 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
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## Papers
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+### ImageNet Classification
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+ * Microsoft (PReLu/Weight Initialization) [[Paper]](http://arxiv.org/pdf/1502.01852v1)
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+ * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852.
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+ * Batch Normalization [[Paper]](http://arxiv.org/pdf/1502.03167v3) [[Paper-2]](
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+ * Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167.
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+ * GoogLeNet [[Paper]](http://arxiv.org/pdf/1409.4842v1)
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+ * Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR 2015.
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+ * AlexNet [[Paper]](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf)
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+ * Krizhevsky, A., Sutskever, I. and Hinton, G. E, ImageNet Classification with Deep Convolutional Neural Networks
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+NIPS 2012.
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+
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### Image Captioning
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* Baidu/UCLA [[Paper]](http://arxiv.org/pdf/1410.1090v1)
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* Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, Explain Images with Multimodal Recurrent Neural Networks, arXiv:1410.1090 (2014).
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* Compression Artifacts Reduction by a Deep Convolutional Network [[Paper-arXiv15]](http://arxiv.org/pdf/1504.06993v1)
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* Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993
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+## Tutorials
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+ * [CVPR 2014] [Tutorial on Deep Learning in Computer Vision](https://sites.google.com/site/deeplearningcvpr2014/)
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