Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation. However, this assumption is often infeasible owing to confidentiality issues or memory constraints on mobile devices. Some recently developed approaches do not require source images during adaptation, but they show limited performance on perturbed images. To address these problems, we propose a novel source-free UDA method that uses only a pre-trained source model and unlabeled target images. Our method captures the aleatoric uncertainty by incorporating data augmentation and trains the feature generator with two consistency objectives. The feature generator is encouraged to learn consistent visual features away from the decision boundaries of the head classifier. Thus, the adapted model becomes more robust to image perturbations. Inspired by self-supervised learning, our method promotes inter-space alignment between the prediction space and the feature space while incorporating intra-space consistency within the feature space to reduce the domain gap between the source and target domains. We also consider epistemic uncertainty to boost the model adaptation performance. Extensive experiments on popular UDA benchmark datasets demonstrate that the proposed source-free method is comparable or even superior to vanilla UDA methods. Moreover, the adapted models show more robust results when input images are perturbed.

翻译：大多数无监督域适应（UDA）方法假设模型适应过程中有标签的源域图像可用。然而，由于保密性问题或移动设备的存储限制，这一假设往往难以实现。近年来发展的部分方法在适应过程中无需源图像，但对扰动图像的适应性能有限。为解决上述问题，本文提出一种新颖的无源UDA方法，该方法仅需预训练源模型与无标签目标域图像。通过引入数据增强捕获偶然不确定性，并采用两种一致性目标训练特征生成器。特征生成器被鼓励学习远离分类器决策边界的稳定视觉特征，从而使适应模型对图像扰动更具鲁棒性。受自监督学习启发，本方法在促进预测空间与特征空间跨空间对齐的同时，通过特征空间内的一致性约束缩小源域与目标域之间的域差异。我们进一步考虑认知不确定性以提升模型适应性能。在主流UDA基准数据集上的大量实验表明，所提出的无源方法与传统UDA方法性能相当甚至更优。此外，当输入图像遭受扰动时，适应后的模型展现出更强的鲁棒性。