Robust Unsupervised Domain Adaptation through Negative-View Regularization.

In the realm of Unsupervised Domain Adaptation (UDA), Vision Transformers (ViTs) have recently demonstrated remarkable adaptability surpassing that of traditional Convolutional Neural Networks (CNNs). Nevertheless, the patch-based structure of ViTs heavily relies on local features within image patches, potentially leading to reduced robustness when confronted with out-of-distribution (OOD) samples. To address this concern, we introduce a novel regularizer tailored specifically for UDA. By leveraging negative views, i.e. target-domain samples applied by negative augmentations, we make the learning process more intricate, thereby preventing models from taking shortcuts in spatial context recognition. We present a novel loss function, rooted in contrastive principles, to effectively distinguish between the negative views and original target samples. By integrating this novel regularizer with existing UDA methodologies, we guide ViTs to prioritize context relationships among local patches, thereby enhancing the robustness of ViTs. Our proposed Negative View-based Contrastive (NVC) regularizer substantially boosts the performance of baseline UDA methods across diverse benchmark datasets. Furthermore, we release new dataset, Retail-71, comprising 71 classes of images commonly encountered in retail stores. Through comprehensive experimentation, we showcase the effectiveness of our approach on traditional benchmarks as well as the novel retail domain. These results substantiate the robust adaptation capabilities of our proposed method. Our method is implemented at our repository.