Universal domain adaptation (UniDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain without any assumptions of the label sets, which requires distinguishing the unknown samples from the known ones in the target domain. A main challenge of UniDA is that the nonidentical label sets cause the misalignment between the two domains. Moreover, the domain discrepancy and the supervised objectives in the source domain easily lead the whole model to be biased towards the common classes and produce overconfident predictions for unknown samples. 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 which fully exploits the distribution of the target samples in the latent space. Then, based on the estimation, we propose a novel neighbors searching scheme in a 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 the source domain to avoid the influence of domain misalignment. Secondly, this paper well balances the confidences of predictions for both known and unknown samples through an uncertainty-guided margin loss based on the confidences of discovered unknown samples, which can reduce the gap between the intra-class variances 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）旨在将标记源域的知识迁移到无标记目标域，而不对标签集做任何假设，这需要区分目标域中的未知样本与已知样本。UniDA的一个主要挑战是，非相同的标签集会导致两个域之间的错位。此外，域差异和源域中的监督目标容易使整个模型偏向于公共类别，并对未知样本产生过度自信的预测。为解决上述挑战性问题，我们提出了一种新的不确定性引导的UniDA框架。首先，我们引入了一个目标样本属于未知类别的概率的经验估计，该估计充分利用了目标样本在潜在空间中的分布。然后，基于该估计，我们提出了一种在线性子空间中使用δ滤波器的新颖邻居搜索方案，以估计目标样本的不确定性分数并发现未知样本。它充分利用了目标样本与源域中邻居之间的关系，避免了域错位的影响。其次，本文通过基于发现的未知样本置信度的不确定性引导边界损失，很好地平衡了已知和未知样本预测的置信度，这可以减少已知类别相对于未知类别的类内方差差距。最后，在三个公共数据集上的实验表明，我们的方法显著优于现有的最先进方法。