Workshop: Workshop on Distribution Shifts: Connecting Methods and Applications

Dropout Disagreement: A Recipe for Group Robustness with Fewer Annotations

Tyler LaBonte · Abhishek Kumar · Vidya Muthukumar

Abstract: Empirical risk minimization (ERM) of neural networks can cause over-reliance on spurious correlations and poor generalization on minority groups. Deep feature reweighting (DFR) improves group robustness via last-layer retraining, but it requires full group and class annotations for the reweighting dataset. To eliminate this impractical requirement, we propose a one-shot active learning method which constructs the reweighting dataset with the disagreement points between the ERM model with and without dropout activated. Our experiments show our approach achieves 95% of DFR performance on the Waterbirds and CelebA datasets despite using no group annotations and up to $7.5\times$ fewer class annotations.

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