SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation

arXiv:2403.10834v1 Announce Type: cross
Abstract: In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domains without requiring access to source domain data. Although the potential benefits of applying data augmentation to SFDA are attractive, several challenges arise such as the dependence on prior knowledge of class-preserving transformations and the increase in memory and computational requirements. In this paper, we propose Source-free Domain …

abstract adapt arxiv augmentation benefits cs.ai cs.cv cs.lg data deep learning domain domain adaptation domains face free shift through type vulnerability