Domain adaptation has shown appealing performance by leveraging knowledge from a source domain with rich annotations. However, for a specific target task, it is cumbersome to collect related and high-quality source domains. In real-world scenarios, large-scale datasets corrupted with noisy labels are easy to collect, stimulating a great demand for automatic recognition in a generalized setting, i.e., weakly-supervised partial domain adaptation (WS-PDA), which transfers a classifier from a large source domain with noises in labels to a small unlabeled target domain. As such, the key issues of WS-PDA are: 1) how to sufficiently discover the knowledge from the noisy labeled source domain and the unlabeled target domain, and 2) how to successfully adapt the knowledge across domains. In this paper, we propose a simple yet effective domain adaptation approach, termed as self-paced transfer classifier learning (SP-TCL), to address the above issues, which could be regarded as a well-performing baseline for several generalized domain adaptation tasks. The proposed model is established upon the self-paced learning scheme, seeking a preferable classifier for the target domain. Specifically, SP-TCL learns to discover faithful knowledge via a carefully designed prudent loss function and simultaneously adapts the learned knowledge to the target domain by iteratively excluding source examples from training under the self-paced fashion. Extensive evaluations on several benchmark datasets demonstrate that SP-TCL significantly outperforms state-of-the-art approaches on several generalized domain adaptation tasks.

翻译：域自适应通过利用带有丰富标注的源域知识展现了引人注目的性能。然而，针对特定目标任务，收集相关且高质量的源域通常十分繁琐。在现实场景中，带有噪声标签的大规模数据集易于收集，这激发了对广义设置下自动识别的巨大需求，即弱监督部分域自适应（WS-PDA）。该方法旨在将分类器从带有标签噪声的大规模源域迁移至小型无标注目标域。因此，WS-PDA的关键问题在于：1）如何充分从带有噪声标签的源域和无标注目标域中发现知识；2）如何成功实现跨域知识迁移。本文提出了一种简单而有效的域自适应方法，称为自步调迁移分类器学习（SP-TCL），以解决上述问题。该方法可视为多种广义域自适应任务的高性能基线模型。所提模型基于自步调学习框架构建，旨在为目标域寻找更优的分类器。具体而言，SP-TCL通过精心设计的稳健损失函数学习发现可靠知识，同时以自步调方式迭代排除训练中的源域样本，将习得的知识适配至目标域。在多个基准数据集上的广泛实验表明，SP-TCL在多项广义域自适应任务上显著优于当前最先进的方法。