MaskTune: MitigatingSpuriousCorrelationsby ForcingtoExplore

This workproposesMaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTuneforces the trained model to explore new features during asingleepochfinetuning bymasking previously discoveredfeatures.MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, suchasannotating spurious features orlabels forsubgroup samples in a dataset.