Vision Transformers in Domain Adaptation and Generalization: A Study of Robustness

arXiv:2404.04452v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenge cs.ai cs.cv data deep learning distribution domain domain adaptation performance robustness study training transformers type validation vision vision transformers