计算机科学
过度拟合
判别式
对抗制
人工智能
模式识别(心理学)
面部识别系统
面子(社会学概念)
领域(数学分析)
不变(物理)
支持向量机
机器学习
数学
数学物理
人工神经网络
数学分析
社会学
社会科学
作者
Zongcai Sun,Chaoyou Fu,Mandi Luo,Ran He
标识
DOI:10.1109/ijcb52358.2021.9484335
摘要
Heterogeneous face recognition (HFR) is quite challenging due to the large discrepancy introduced by cross-domain face images. The limited number of paired face images results in a severe overfitting problem in existing methods. To tackle this issue, we proposes a novel self-augmentation method named Mixed Adversarial Examples and Logits Replay (MAELR). Concretely, we first generate adversarial examples, and mix them with clean examples in an interpolating way for data augmentation. Simultaneously, we extend the definition of the adversarial examples according to cross-domain problems. Benefiting from this extension, we can reduce domain discrepancy to extract domain-invariant features. We further propose a diversity preserving loss via logits replay, which effectively uses the discriminative features obtained on the large-scale VIS dataset. In this way, we improve the feature diversity that can not be obtained from mixed adversarial examples methods. Extensive experiments demonstrate that our method alleviates the over-fitting problem, thus significantly improving the recognition performance of HFR.
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