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.

翻译：通用域自适应（UniDA）旨在无需任何标签集先验知识的情况下，将源域中通用类别的知识迁移至目标域，这需要区分目标域中的已知样本和未知样本。UniDA的主要挑战在于两域标签空间的不对称性导致域间对齐失效。为解决上述难题，我们提出了一种新的不确定性引导的UniDA框架。首先，我们利用目标样本分布，对目标样本属于未知类别的概率进行了经验性估计。接着，基于该估计，我们提出了一种在具有δ滤波器的线性子空间中进行邻域搜索的新方法，以估算目标样本的不确定性得分并发现未知样本。该方法充分利用目标样本与其源域邻域之间的关系，从而避免域不对齐的影响。其次，本文通过基于所发现未知样本预测的不确定性引导边际损失，有效平衡了已知和未知样本的预测置信度，从而能够减小已知类别相对于未知类别的类内方差差距。最后，在三个公开数据集上的实验表明，我们的方法显著优于现有最先进方法。