Breaking Spurious Correlations via Generative Randomization and Cross-Variant Self-Supervised Learning
This work proposes a two-stage framework that uses generative intervention to explicitly learn background-invariant visual representations, and introduces Cross-Variant Self-Supervised Learning, where variants of the same object under different backgrounds form positive pairs in a contrastive objective.