  Despite the recent success of deep neural networks, there remains a need for
effective methods to enhance domain generalization using vision transformers.
In this paper, we propose a novel domain generalization technique called Robust
Representation Learning with Self-Distillation (RRLD) comprising i)
intermediate-block self-distillation and ii) augmentation-guided
self-distillation to improve the generalization capabilities of
transformer-based models on unseen domains. This approach enables the network
to learn robust and general features that are invariant to different
augmentations and domain shifts while effectively mitigating overfitting to
source domains. To evaluate the effectiveness of our proposed method, we
perform extensive experiments on PACS and OfficeHome benchmark datasets, as
well as an industrial wafer semiconductor defect dataset. The results
demonstrate that RRLD achieves robust and accurate generalization performance.
We observe an average accuracy improvement in the range of 1.2% to 2.3% over
the state-of-the-art on the three datasets.
