  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 https://github.com/Vision.

Computer Vision and Pattern Recognition