Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing

Summary

arXiv:2606.02830v1 Announce Type: new Abstract: Real-world datasets often contain spurious correlations that are not causally related to the target label. When such correlations dominate the majority of training samples, models tend to rely on them, leading to misclassification of minority samples that do not exhibit the same spurious patterns. While a potential approach is to select subsets of data to better represent the minority samples, this may require access to group labels, which are typically unknown.