Source-Guided Adversarial Learning and Data Augmentation for Domain Generalization

Abstract

Domain generalization aims to learn a generalized feature representation across multiple source domains so as to adapt to an unseen target domain. In this paper, we focus on image classification and propose a domain generalization framework with two cooperative ideas. First, to leverage the generalization capability, we propose a novel data augmentation method through a feature generator. The generated latent data not only preserve class-discriminative image content but also exhibit a diverse range of styles covering multiple source domains. Second, to enhance the class discriminability, we resort to the prominent adversary learning under a novel source-guided distribution constraint. We initialize the distribution prior with a mixture of Gaussians and then refer to source domains to update the constraint along with the model learning. The two models, data augmentation and adversary learning, are jointly trained to support each other and to boost the overall classification performance. Experimental results on several benchmark cross-domain datasets show that the proposed method significantly outperforms previous methods.