Maximum open-set entropy optimization via uncertainty measure for universal domain adaptation

Abstract

Universal Domain Adaptation (UniDA) is a technology that enables the intelligent model to transfer knowledge learned from labeled source domains to related but unlabeled target domains without any prior label set relationship. The key to UniDA lies in rejecting target domain-specific “unknown” samples to achieve domain alignment on shared classes. In this paper, we propose the Maximum Open-set Entropy Optimization via uncertainty measure to adaptively reject “unknown” samples. Specifically, MOEO sets a transition region within the most easily confused class in the open-set space. We optimize the maximum open-set entropy of simple samples outside the transition region to further improve their confidence and separate difficult samples within the transition region by aggregating similar neighbors. Accordingly, a secure boundary is formed between shared samples and “unknown” samples, promoting domain alignment within shared classes. Experiments on four benchmarks show that MOEO outperforms the previous state-of-the-art significantly.