Discriminative Mutual Learning for Multi-target Domain Adaptation

Abstract

Unsupervised domain adaptation (UDA) has attracted much attention among those seeking to transfer a model from a labeled source domain to an unlabeled target domain. Many effective algorithms for single target domain adaptation (STDA) have been designed, however, STDA cannot satisfy the scenarios of transferring simultaneously to multiple target domains or transferring to a blending target domain. This paper proposes a novel discriminative mutual learning method for multi-target domain adaptation covering both blending target domain adaptation (BTDA) and multiple target domain adaptation (MTDA). Two key points are considered in the proposed method: one is to learn discriminative features for better prediction, and the other is to self-train the model with pseudo-labeled target data based on distance information. These two aspects are integrated through a mutual learning strategy via two different classifiers. According to extensive experiments on three domain adaptation benchmarks, the proposed method demonstrates the state-of-the-art performance in both BTDA and MTDA settings.