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arXiv · 2210.15836

Domain Generalization through the Lens of Angular Invariance

Yujie Jin·Xu Chu·Yasha Wang·Wenwu Zhu
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Abstract

Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. A common pervasive theme in existing DG literature is domain-invariant representation learning with various invariance assumptions. However, prior works restrict themselves to a radical assumption for realworld challenges: If a mapping induced by a deep neural network (DNN) could align the source domains well, then such a mapping aligns a target domain as well. In this paper, we simply take DNNs as feature extractors to relax the requirement of distribution alignment. Specifically, we put forward a novel angular invariance and the accompanied norm shift assumption. Based on the proposed term of invariance, we propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN). The optimization objective of AIDGN is developed with a von-Mises Fisher (vMF) mixture model. Extensive experiments on multiple DG benchmark datasets validate the effectiveness of the proposed AIDGN method.

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Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu. 2022-10-28. Domain Generalization through the Lens of Angular Invariance. https://arxiv.org/abs/2210.15836

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