利用样本间亲和性实现可感知性感知的通用域适应
Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation
AI总结：
针对通用域适应中现有方法忽略已知与未知样本间亲和性的问题，提出利用样本间亲和性的新型框架，通过可感知性标注方案和辅助损失优化，在四个公开数据集上性能超越现有最优方法。
摘要：
通用域适应（UniDA）旨在在不具备标签集任何先验知识的情况下，将源域中共有类别的知识迁移到目标域，这需要在目标域中区分未知样本与已知样本。现有方法通常聚焦于将目标样本分类到某个源类别中，而非区分已知和未知样本，这忽略了已知与未知样本间的样本间亲和性，可能导致性能欠佳。针对这一问题，我们提出一种新型UDA框架，该框架对这类样本间亲和性加以利用。具体而言，我们引入一种基于可感知性的标注方案，可分为两个步骤：1）基于样本邻域内在结构的可感知性引导的已知与未知样本检测，我们利用亲和矩阵的第一奇异向量来获取每个目标样本的可感知性。2）基于邻域一致性的标签精炼以重新标注目标样本，我们根据每个目标样本预测结果的邻域一致性来精炼其标签。随后，基于这两个步骤的辅助损失被用于降低未知与已知目标样本之间的样本间亲和性。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的最优方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
