发表机构
School of Software Engineering, South China University of Technology; School of Future Technology, South China University of Technology; College of Computer Science, Chongqing University（华南理工大学软件工程学院; 华南理工大学未来技术学院; 重庆大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对通用域适应（UniDA）场景，推导其泛化误差上界，提出JAUA算法并结合渐进式伪标签方法，在6个公开图像数据集上验证了该算法的优越性。
AI 中文摘要
无监督域适应（UDA）已在机器学习、模式识别与计算机视觉领域受到广泛关注。传统UDA学习通常假设源域与目标域的标签空间完全相同，仅需解决两域间存在的样本分布漂移问题。但在实际应用中，两域间的标签空间可能存在差异，此时域间既存在样本分布漂移，又存在类别空间差异，即通用域适应（UniDA）学习场景。目前现有研究很少为通用域适应提供理论分析，本文给出通用域适应的泛化误差上界。根据该泛化误差界，提出一种名为通用域适应的联合分布对齐（JAUA）的新型UniDA算法，通过最小化卡方散度计算的分布差异来对齐联合分布；还提出一种渐进式伪标签方法，为未标记的目标样本分配伪标签。在6个公开图像数据集上的实验结果表明，JAUA在处理UniDA问题时具有优越性。
英文摘要
Unsupervised domain adaptation (UDA) has been widely concerned in the fields of machine learning, pattern recognition, and computer vision. Traditional UDA learning usually assumes that the label spaces of the source and target domains are exactly the same and only needs to solve the problem of sample distribution drift existing between two domains. However, in real world applications, the label spaces between two domains may be different. In this case, there are both sample distribution drift and class spatial difference between domains, namely Universal Domain Adaptation (UniDA) learning scenario. At present, existing works rarely offer theoretical analysis for universal domain adaptation. In this paper, we provide an upper bound of the generalization error for universal domain adaptation. According to the proposed generalization error bound, we propose a novel UniDA algorithm called Joint Distribution Alignment for Universal Domain Adaptation (JAUA), which aligns the joint distributions by minimizing the distribution discrepancy calculated by Chi-Square divergence. Furthermore, we propose a progressive pseudo-labeling method to assign the pseudo labels to unlabeled target samples. The experiment results on six public image datasets demonstrate the superiority of JAUA in handling the UniDA problem.
