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	Sparsifying Neural Network Connections for Face Recognition
	Yi Sun; Xiaogang Wang; Xiaoou Tang
	2016
会议名称	CVPR2016
会议地点	美国
英文摘要	This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as s- parse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse Con- vNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight se- lection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure (26%-76% of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-the- art DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12% of the original parameters.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10022]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Yi Sun,Xiaogang Wang,Xiaoou Tang. Sparsifying Neural Network Connections for Face Recognition[C]. 见:CVPR2016. 美国.

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