  Neural image classifiers are known to undergo severe performance degradation
when exposed to inputs that are sampled from environmental conditions that
differ from their training data. Given the recent progress in Text-to-Image
(T2I) generation, a natural question is how modern T2I generators can be used
to simulate arbitrary interventions over such environmental factors in order to
augment training data and improve the robustness of downstream classifiers. We
experiment across a diverse collection of benchmarks in single domain
generalization (SDG) and reducing reliance on spurious features (RRSF),
ablating across key dimensions of T2I generation, including interventional
prompting strategies, conditioning mechanisms, and post-hoc filtering. Our
extensive empirical findings demonstrate that modern T2I generators like Stable
Diffusion can indeed be used as a powerful interventional data augmentation
mechanism, outperforming previously state-of-the-art data augmentation
techniques regardless of how each dimension is configured.
