Contrastive Diversity Augmentation for Single Domain Generalization

Single domain generalization aims to train a model on a single source domain that generalizes to unseen target domains, which is critical in multimedia applications. Current methods typically use adversarial data augmentation to enrich the source domain distribution with novel samples. However, these methods typically rely on labeled data and require adversarial training between generators and classifiers, which may limit sample diversity and introduce spurious correlations. To tackle these problems, we propose a method that integrates Contrastive clustering regularization with an Unsupervised Diversity Augmentation (UDA), termed C-UDA. Specifically, UDA is a flexible and general framework in which two customized models iteratively optimize a novel adversarial loss to enable fully unsupervised data augmentation. Within UDA, we design a lightweight generator that diversifies each input image along three distinct visual attributes. Based on both original and augmented images, we further introduce contrastive clustering regularization to encourage the model to learn domain-invariant representations, resulting in robust decision boundaries. Extensive experiments on four challenging benchmarks demonstrate that C-UDA significantly outperforms 22 state-of-the-art methods. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Ruiding1/C-UDA</uri>.