Improved Worst-Group Robustness via Classifier Retraining on Independent Splits
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High-capacity deep neural networks (DNNs) trained with Empirical Risk Minimization (ERM) often suffer from poor worst-group accuracy despite good on-average performance, where worst-group accuracy measures a model's robustness towards certain subpopulations of the input space. Spurious correlations and memorization behaviors of ERM trained DNNs are typically attributed to this degradation in performance. We develop a method, called CRIS, that address these issues by performing robust classifier retraining on independent splits of the dataset. This results in a simple method that improves upon state-of-the-art methods, such as Group DRO, on standard datasets while relying on much fewer group labels and little additional hyperparameter tuning.
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