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。

持续源自由非监督域自适应

Continual Source-Free Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain
adaptation (UDA), which transfers knowledge learned from a rich-label dataset
to the unlabeled target dataset, is gaining increasing popularity. While
extensive studies have been devoted to improving the model accuracy on target
domain, an important issue of model robustness is neglected. To make things
worse, conventional adversarial training (AT) methods for improving model
robustness are inapplicable under UDA scenario since they train models on
adversarial examples that are generated by supervised loss function. In this
paper, we present a new meta self-training pipeline, named SRoUDA, for
improving adversarial robustness of UDA models. Based on self-training
paradigm, SRoUDA starts with pre-training a source model by applying UDA
baseline on source labeled data and taraget unlabeled data with a developed
random masked augmentation (RMA), and then alternates between adversarial
target model training on pseudo-labeled target data and finetuning source model
by a meta step. While self-training allows the direct incorporation of AT in
UDA, the meta step in SRoUDA further helps in mitigating error propagation from
noisy pseudo labels. Extensive experiments on various benchmark datasets
demonstrate the state-of-the-art performance of SRoUDA where it achieves
significant model robustness improvement without harming clean accuracy. Code
is available at this https URL

本文提出了一种新的元自我训练流程 SRoUDA，通过自训练的范例，SRoUDA 允许直接将 AT 纳入到 UDA 中，并且，在 SRoUDA 中的元步骤进一步有助于由嘈杂的伪标签导致的误差传播的缓解。经过各种基准数据集的广泛实验表明，SRoUDA 在不损害清洁度准确性的情况下实现了显着的模型稳健性的提高。