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Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
Based on our observations, models trained with ERM usually highly attend to either the causal components or the components having a high spurious correlation with the label (especially in datapoints on which models have a high confidence).
Annotation-Free Group Robustness via Loss-Based Resampling
In order to provide group robustness without such annotations, we propose a new method, called loss-based feature re-weighting (LFR), in which we infer a grouping of the data by evaluating an ERM-pre-trained model on a small left-out split of the training data.
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