To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain. However, the existing UniDA methods rely on source samples with correct annotations. Due to the limited resources in the real world, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain in some applications. As a result, we propose a novel realistic scenario named Noisy UniDA, in which classifiers are trained using noisy labeled data from the source domain as well as unlabeled domain data from the target domain that has an uncertain class distribution. A multi-head convolutional neural network framework is proposed in this paper to address all of the challenges faced in the Noisy UniDA at once. Our network comprises a single common feature generator and multiple classifiers with various decision bounds. We can detect noisy samples in the source domain, identify unknown classes in the target domain, and align the distribution of the source and target domains by optimizing the divergence between the outputs of the various classifiers. The proposed method outperformed the existing methods in most of the settings after a thorough analysis of the various domain adaption scenarios. The source code is available at \url{https://github.com/YU1ut/Divergence-Optimization}.

翻译：为将已标注源域学到的知识迁移至未标注目标域，诸多研究聚焦于通用域自适应（UniDA），该方法不对源域和目标域的标签集施加约束。然而，现有UniDA方法依赖具有正确标注的源域样本。由于现实世界资源有限，某些应用中难以获取大量完全洁净的标注源域数据。为此，我们提出名为"含噪通用域自适应"（Noisy UniDA）的新型现实场景，其分类器利用源域含噪标注数据及具有不确定类别分布的目标域未标注数据进行训练。本文提出一种多头卷积神经网络框架，以同步应对含噪通用域自适应面临的所有挑战。该网络由单个共享特征生成器与多个具有不同决策边界的分类器组成。通过优化各分类器输出间的散度，我们可检测源域含噪样本、识别目标域未知类别，并对齐源域与目标域的分布。经对多种域自适应场景的深入分析，所提方法在大多数设置下优于现有方法。相关源代码已发布于 \url{https://github.com/YU1ut/Divergence-Optimization}。