  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.
