Source-free unsupervised domain adaptation with maintaining model balance and diversity

Abstract

Source-free unsupervised domain adaptation (SFUDA) uses the knowledge learned from the source domain pre-trained model to perform the task on the target domain without directly accessing the source domain. However, the source domain pre-trained model is biased towards the source domain but deviates from the target domain, so its pseudo-label on the target domain tends to have a large noise. Thus, we should avoid using noisy pseudo-labels to train the model. In this article, we propose a novel kind of source-free unsupervised domain adaptation with maintaining model balance and diversity (SFMBD), which designs a target domain-specific classifier whose classification boundary is far from the high-density area of the target domain feature distribution. In addition, we keep the model balance and promote the model diversity while maintaining its ability to discriminate the target domain. Experimental evaluation of multiple benchmark datasets illustrates the effectiveness of our proposed approach on SFUDA.