Vision Transformers in Domain Adaptation and Generalization: A Study of
  Robustness
Shadi Alijani Jamil Fayyad Homayoun Najjaran
Deep learning models are often evaluated in scenarios where the data
distribution is different from those used in the training and validation
phases. The discrepancy presents a challenge for accurately predicting the
performance of models once deployed on the target distribution. Domain
adaptation and generalization are widely recognized as effective strategies for
addressing such shifts, thereby ensuring reliable performance. The recent
promising results in applying vision transformers in computer vision tasks,
coupled with advancements in self-attention mechanisms, have demonstrated their
significant potential for robustness and generalization in handling
distribution shifts. Motivated by the increased interest from the research
community, our paper investigates the deployment of vision transformers in
domain adaptation and domain generalization scenarios. For domain adaptation
methods, we categorize research into feature-level, instance-level, model-level
adaptations, and hybrid approaches, along with other categorizations with
respect to diverse strategies for enhancing domain adaptation. Similarly, for
domain generalization, we categorize research into multi-domain learning,
meta-learning, regularization techniques, and data augmentation strategies. We
further classify diverse strategies in research, underscoring the various
approaches researchers have taken to address distribution shifts by integrating
vision transformers. The inclusion of comprehensive tables summarizing these
categories is a distinct feature of our work, offering valuable insights for
researchers. These findings highlight the versatility of vision transformers in
managing distribution shifts, crucial for real-world applications, especially
in critical safety and decision-making scenarios.