Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization

In ___domain generalization (DG), the target ___domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) target ___domain during inference. This is a difficult problem, and despite active studies in recent years, it remains a great challenge. In this paper, we take a simple yet effective approach to tackle this issue. We propose test-time style shifting, which shifts the style of the test sample (that has a large style gap with the source ___domains) to the nearest source ___domain that the model is already familiar with, before making the prediction. This strategy enables the model to handle any target ___domains with arbitrary style statistics, without additional model update at test-time. Additionally, we propose style balancing, which provides a great platform for maximizing the advantage of test-time style shifting by handling the DG-specific imbalance issues. The proposed ideas are easy to implement and successfully work in conjunction with various other DG schemes. Experimental results on different datasets show the effectiveness of our methods.