Neighborhood-based credibility anchor learning for universal domain adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain in the presence of distribution shift and class mismatch. Most existing works design threshold-relied methods to reject target private classes by carefully-proposed uncertainty scoring functions which are very sensitive to thresholds. To overcome this problem, a few threshold-free methods are proposed but ignore the neighborhood structure information of the target domain, leading to poor performance. In this paper, we propose Neighborhood-based Credibility Anchor Learning (NCAL), a new threshold-free framework that fully mines the neighborhood structure information to explore better target representations. NCAL contains three key components: a class anchor learning module to learn target class distribution, a credibility-weighted conditional adversarial module to learn class-invariant features of common classes, and an open-set neighborhood clustering module to learn well-clustered features. Extensive experiments demonstrate that our method outperforms the state-of-the-art.