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arXiv · 2409.14637

Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting

Humza Wajid Hameed·Geraldin Nanfack·Eugene Belilovsky
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Abstract

Spurious correlations are a major source of errors for machine learning models, in particular when aiming for group-level fairness. It has been recently shown that a powerful approach to combat spurious correlations is to re-train the last layer on a balanced validation dataset, isolating robust features for the predictor. However, key attributes can sometimes be discarded by neural networks towards the last layer. In this work, we thus consider retraining a classifier on a set of features derived from all layers. We utilize a recently proposed feature selection strategy to select unbiased features from all the layers. We observe this approach gives significant improvements in worst-group accuracy on several standard benchmarks.

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Humza Wajid Hameed, Geraldin Nanfack, Eugene Belilovsky. 2024-09-23. Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting. https://arxiv.org/abs/2409.14637

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