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. Code is available at https://github.com/mc-lan/SP-TCL .

中文翻译：

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学习发现知识：弱监督的部分领域适应方法


通过利用来自具有丰富注释的源域的知识，域适应已显示出吸引人的性能。然而，对于特定的目标任务，收集相关且高质量的源域是很麻烦的。在现实场景中，被噪声标签损坏的大规模数据集很容易收集，刺激了对广义设置中自动识别的巨大需求，即弱监督部分域适应（WS-PDA），它将分类器从一个带有噪声标签的大源域到一个小的未标记目标域。因此，WS-PDA的关键问题是：1）如何从有噪声的标记源域和未标记目标域中充分发现知识，2）如何成功地跨域调整知识。在本文中，我们提出了一种简单而有效的领域适应方法，称为自定进度转移分类器学习（SP-TCL）来解决上述问题，该方法可以被视为几个广义领域适应任务的良好表现的基线。所提出的模型是建立在自定进度学习方案的基础上的，为目标领域寻找更好的分类器。具体来说，SP-TCL 通过精心设计的谨慎损失函数学习发现忠实的知识，并同时通过在自定进度的方式下迭代地从训练中排除源示例来使学到的知识适应目标领域。对多个基准数据集的广泛评估表明，SP-TCL 在多个广义域适应任务上显着优于最先进的方法。代码可在https://github.com/mc-lan/SP-TCL 。