Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set, which requires distinguishing in the target domain the unknown samples from the known ones. Recent methods usually focused on categorizing a target sample into one of the source classes rather than distinguishing known and unknown samples, which ignores the inter-sample affinity between known and unknown samples and may lead to suboptimal performance. Aiming at this issue, we propose a novel UDA framework where such inter-sample affinity is exploited. Specifically, we introduce a knowability-based labeling scheme which can be divided into two steps: 1) Knowability-guided detection of known and unknown samples based on the intrinsic structure of the neighborhoods of samples, where we leverage the first singular vectors of the affinity matrices to obtain the knowability of every target sample. 2) Label refinement based on neighborhood consistency to relabel the target samples, where we refine the labels of each target sample based on its neighborhood consistency of predictions. Then, auxiliary losses based on the two steps are used to reduce the inter-sample affinity between the unknown and the known target samples. Finally, experiments on four 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，将源域中公共类别的知识迁移至目标域，这要求区分目标域中的已知样本与未知样本。近期方法通常侧重于将目标样本归类至源类别之一，而非区分已知样本与未知样本，从而忽略了已知与未知样本间的样本间亲和性，可能导致次优性能。针对此问题，我们提出一种新型UDA框架，充分利用此类样本间亲和性。具体而言，我们引入一种基于可知识别的标签方案，该方案分为两步：1）基于样本邻域内在结构的可知识别引导的已知与未知样本检测，其中利用亲和矩阵的第一奇异向量获取每个目标样本的可知识别性；2）基于邻域一致性的标签精炼，通过预测的邻域一致性修正各目标样本的标签。随后，基于这两步的辅助损失被用于降低未知与已知目标样本间的样本间亲和性。最后，在四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。