Unsupervised domain adaptation (UDA) aims to learn a model for unlabeled data on a target domain by transferring knowledge from a labeled source domain. In the traditional UDA setting, labeled source data are assumed to be available for the use of model adaptation. Due to the increasing concerns for data privacy, source-free UDA is highly appreciated as a new UDA setting, where only a trained source model is assumed to be available, while the labeled source data remain private. However, exposing details of the trained source model for UDA use is prone to easily committed white-box attacks, which brings severe risks to the source tasks themselves. To address this issue, we advocate studying a subtly different setting, named Black-Box Unsupervised Domain Adaptation (B2UDA), where only the input-output interface of the source model is accessible in UDA; in other words, the source model itself is kept as a black-box one. To tackle the B2UDA task, we propose a simple yet effective method, termed Iterative Noisy Label Learning (IterNLL). IterNLL starts with getting noisy labels of the unlabeled target data from the black-box source model. It then alternates between learning improved target models from the target subset with more reliable labels and updating the noisy target labels. Experiments on benchmark datasets confirm the efficacy of our proposed method. Notably, IterNLL performs comparably with methods of the traditional UDA setting where the labeled source data are fully available.

本篇论文研究了源领域无标签数据的黑盒非监督域适应 (B$^2$UDA) 问题，在此基础上提出了一种名为 Iterative Learning with Noisy Labels (IterLNL) 的简单有效方法，通过黑盒模型作为噪声标签工具进行噪声标注和学习，迭代解决该问题。实验表明，无需源数据和源模型，IterLNL 的表现可与传统有标签源数据的 UDA 方法相媲美。

黑盒源模型的无监督领域自适应