Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting
<|reference_start|>Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting: 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.<|reference_end|>
@article{hameed2024not,
    title={Not Only the Last-Layer Features for Spurious Correlations: All Layer
  Deep Feature Reweighting},
    author={Humza Wajid Hameed, Geraldin Nanfack and Eugene Belilovsky},
    journal={arXiv preprint arXiv:2409.14637},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14637},
    primaryClass={cs.LG cs.AI}
}