Provably Uncertainty-Guided Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from source domain to target domain without any prior knowledge on the label set, which requires to distinguish the unknown samples from the known ones in the target domain. A main challenge of UniDA is that the unequal label spaces of both domains causes the misalignment between two domains.To address the above challenging problems, we propose a new uncertainty-guided UniDA framework. Firstly, we introduce an empirical estimation of the probability of a target sample belonging to the unknown class with exploiting the distribution of target samples. Then, based on the estimation, we propose a novel neighbors searching method in the linear subspace with a $\delta$-filter to estimate the uncertainty score of a target sample and discover unknown samples. It fully utilizes the relationship between a target sample and its neighbors in source domain to avoid the influence of domain misalignment. Secondly, this paper well balances the confidence of predictions for both known and unknown samples through an uncertainty-guided margin loss based on the predictions of discovered unknown samples, which can reduce the gap between intra-class variance of known classes with respect to the unknown class. Finally, experiments on three public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods.