Existing Source-free Unsupervised Domain Adaptation (SUDA) approaches
inherently exhibit catastrophic forgetting. Typically, models trained on a
labeled source domain and adapted to unlabeled target data improve performance
on the target while dropping performance on the source, which is not available
during adaptation. In this study, our goal is to cope with the challenging
problem of SUDA in a continual learning setting, i.e., adapting to the
target(s) with varying distributional shifts while maintaining performance on
the source. The proposed framework consists of two main stages: i) a SUDA model
yielding cleaner target labels -- favoring good performance on target, and ii)
a novel method for synthesizing class-conditioned source-style images by
leveraging only the source model and pseudo-labeled target data as a prior. An
extensive pool of experiments on major benchmarks, e.g., PACS, Visda-C, and
DomainNet demonstrates that the proposed Continual SUDA (C-SUDA) framework
enables preserving satisfactory performance on the source domain without
exploiting the source data at all.

本研究旨在通过提出一种新型架构，即 C-SUDA，解决现有无源无监督领域自适应在不断学习的情况下存在的灾难性遗忘问题，并在不利于源领域的情况下，仅通过利用假标记的目标数据和源模型来维护源领域的性能，实现更好的目标领域性能。在 PACS、Visda-C 和 DomainNet 等基准数据集上的实验结果表明，C-SUDA 框架能够在完全不利用源数据的情况下，保持对源域的满意性能。