Abstract:Last-layer retraining (LLR) methods -- wherein the last layer of a neural network is reinitialized and retrained on a held-out set following ERM training -- have garnered interest as an efficient approach to rectify dependence on spurious correlations and improve performance on minority groups. Surprisingly, LLR has been found to improve worst-group accuracy even when the held-out set is an imbalanced subset of the training set. We initially hypothesize that this ``unreasonable effectiveness'' of LLR is explained by its ability to mitigate neural collapse through the held-out set, resulting in the implicit bias of gradient descent benefiting robustness. Our empirical investigation does not support this hypothesis. Instead, we present strong evidence for an alternative hypothesis: that the success of LLR is primarily due to better group balance in the held-out set. We conclude by showing how the recent algorithms CB-LLR and AFR perform implicit group-balancing to elicit a robustness improvement.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2512.01766 [cs.LG] |
| (or arXiv:2512.01766v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2512.01766 arXiv-issued DOI via DataCite |
Submission history
From: John Hill [view email]
[v1]
Mon, 1 Dec 2025 15:08:43 UTC (9,798 KB)
[v2]
Wed, 13 May 2026 19:55:26 UTC (15,189 KB)