Domain Dilation for Single Domain Generalization

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This work investigates the Single Domain Generalization (SDG), which generalizes a model from a single source domain to multiple unseen target domains. Most existing SDG methods focus on expanding the source domain by either transforming the source samples into different styles or optimizing adversarial noise perturbations applied to the source samples. However, these methods generate fictitious samples using specific image transformation, resulting in insufficient domain expansion. In this paper, we propose a progressive domain expansion method, namely domain dilation (DD) for SDG. This method dilates the source domain from two perspectives: enriching source domain diversity and generating various pseudo domains. To enrich source domain diversity, we generate fictitious samples with diverse styles. To obtain various pseudo domains, this paper generates pseudo domains with a new distribution by maximizing the domain difference from the source domain. Our method outperforms the state-of-the-art methods on prevalent single domain generalization benchmarks through extensive experiments, offering improved results.