Single Domain Generalization Benchmark: Current State and Challenges

Abstract

Recent advances in deep learning have enabled remarkable success across computer vision tasks using massive datasets, yet the persistent challenge of domain shift-where training and testing distributions differ-continues to degrade model performance. While Domain Generalization techniques aim to learn invariant representations across multiple domains, their reliance on diverse training domains limits applicability in settings where multiple source domains are not available. This survey examines Single Domain Generalization (SDG) approaches that leverage targeted inductive biases to enhance out-of-domain (OOD) performance using a single source domain, addressing key questions regarding their effectiveness across various domain shifts, different model architectures such as CNNs and Vision transformers, and the impact of shape bias for OOD generalization. These questions are crucial as they directly inform the development of robust models that can reliably operate under real-world conditions, guiding the research community in overcoming limitations of current techniques. Our findings indicate that although SDG methods improve performance under highly domain distinguishable stylized shifts, they exhibit limited robustness to background, correlation and corruption shifts on larger datasets, and increased shape bias does not consistently yield enhanced OOD performance, thereby underscoring the need for further research into developing more resilient and generalizable models. For reproducibility, the implementation and code used in our benchmark are available on our official https://github.com/Kavindya98/SDG-Benchmark GitHub repository.