Towards Last-layer Retraining for Group Robustness with Fewer Annotations

• URL: http://arxiv.org/abs/2309.08534v3
• Date: Wed, 15 Nov 2023 04:18:39 GMT
• Title: Towards Last-layer Retraining for Group Robustness with Fewer Annotations
• Authors: Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
• Abstract summary: Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations. Recent deep feature reweighting technique achieves state-of-the-art group robustness via simple last-layer retraining. We show that last-layer retraining can greatly improve worst-group accuracy even when the reweighting dataset has only a small proportion of worst-group data.
• Score: 11.650659637480112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations and poor generalization on minority groups. The recent deep feature reweighting (DFR) technique achieves state-of-the-art group robustness via simple last-layer retraining, but it requires held-out group and class annotations to construct a group-balanced reweighting dataset. In this work, we examine this impractical requirement and find that last-layer retraining can be surprisingly effective with no group annotations (other than for model selection) and only a handful of class annotations. We first show that last-layer retraining can greatly improve worst-group accuracy even when the reweighting dataset has only a small proportion of worst-group data. This implies a "free lunch" where holding out a subset of training data to retrain the last layer can substantially outperform ERM on the entire dataset with no additional data or annotations. To further improve group robustness, we introduce a lightweight method called selective last-layer finetuning (SELF), which constructs the reweighting dataset using misclassifications or disagreements. Our empirical and theoretical results present the first evidence that model disagreement upsamples worst-group data, enabling SELF to nearly match DFR on four well-established benchmarks across vision and language tasks with no group annotations and less than 3% of the held-out class annotations. Our code is available at https://github.com/tmlabonte/last-layer-retraining.

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