Is Last Layer Re-Training Truly Sufficient for Robustness to Spurious Correlations?

TL;DR

This paper investigates the effectiveness of last-layer retraining methods like DFR in improving model robustness against spurious correlations, especially in medical data, and finds that such methods have limitations.

Contribution

The study evaluates DFR's applicability to medical data and analyzes why last-layer retraining may not fully eliminate reliance on spurious features.

Findings

DFR improves worst-group accuracy but remains susceptible to spurious correlations.

Last-layer retraining alone may be insufficient for robustness in realistic data.

Analysis explains the limitations of DFR in addressing spurious features.

Abstract

Models trained with empirical risk minimization (ERM) are known to learn to rely on spurious features, i.e., their prediction is based on undesired auxiliary features which are strongly correlated with class labels but lack causal reasoning. This behavior particularly degrades accuracy in groups of samples of the correlated class that are missing the spurious feature or samples of the opposite class but with the spurious feature present. The recently proposed Deep Feature Reweighting (DFR) method improves accuracy of these worst groups. Based on the main argument that ERM mods can learn core features sufficiently well, DFR only needs to retrain the last layer of the classification model with a small group-balanced data set. In this work, we examine the applicability of DFR to realistic data in the medical domain. Furthermore, we investigate the reasoning behind the effectiveness of last-layer retraining and show that even though DFR has the potential to improve the accuracy of the worst group, it remains susceptible to spurious correlations.