Multiple Domain-Adversarial Ensemble Learning for Domain Generalization

Domain generalization (DG) aims to train a model on multiple source domains which can be well generalized to the unseen target domains. Currently, most DG techniques in vision tasks mainly focus on one of the three research lines (i.e., data manipulation, representation learning, and learning strategy). The direction of combining multiple DG research lines still remains to be studied and explored. In this paper, we propose a unified framework for DG that combines multiple research lines to enhance the generalization ability. To be specific, we combine representative strategies of three research lines: ensemble learning, domain alignment, and data augmentation. A basic framework is built with an ensemble learning strategy to train an expert model in a domain unit. Besides, the ability to learn domain-independent features is enhanced with an adversarial-based domain alignment strategy. Furthermore, a domain transformation network (DoTNet) is introduced, which is expected to generate additional deviated images and further enhance the generalization capability. Extensive experiments on DG datasets demonstrate the effectiveness of our approach.