P2SGrad: Refined Gradients for Optimizing Deep Face Models

Cosine-based softmax losses significantly improve the performance of deep face recognition networks. However, these losses always include sensitive hyper-parameters which can make training process unstable, and it is very tricky to set suitable hyper parameters for a specific dataset. This paper add...

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Bibliographic Details
Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 9898 - 9906
Main Authors:	Zhang, Xiao, Zhao, Rui, Yan, Junjie, Gao, Mengya, Qiao, Yu, Wang, Xiaogang, Li, Hongsheng
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2019
Subjects:	and Body Pose Benchmark testing Biometrics ; Deep Learning ; Optimization Methods; Recognition: Detection Categorization Convergence Environmentally friendly manufacturing techniques Face Face recognition Gesture Gradient methods Neural networks Noise Optimization Retrieval Robustness Training
ISSN:	1063-6919
Online Access:	Get full text
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