In this work, we propose Generative and Explainable Adversarial Data Augmentation (GEADA), a novel framework designed to tackle the single-domain generalization challenge in image classification. The framework consists of two competing components: an augmentor to synthesize diverse yet semantically consistent augmentations, and a projector to learn domain-invariant representations from the augmented samples. The augmentor leverages a generative network for style transformations and an attribution-based cropping module for explainable geometric augmentations. We further incorporate theoretically-grounded contrastive loss functions, inspired by the geometric properties of unit hyperspheres, to promote the diversity of generated augmentations and the robustness of learned representations. Extensive experiments on multiple standard domain generalization benchmarks demonstrate the effectiveness of our approach against domain shifts.
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