Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples

TL;DR

This paper introduces CFKD, a novel framework that generates counterfactuals to improve robustness of image classifiers against spurious correlations without needing group labels.

Contribution

CFKD enables robust model training by generating diverse counterfactuals, enriching data, and correcting decision boundaries without confounder labels, scalable to multiple confounders.

Findings

CFKD outperforms existing methods on five datasets.

It achieves balanced generalization across groups.

Particularly effective in low-data regimes with spurious correlations.

Abstract

Deep learning models remain vulnerable to spurious correlations, leading to so-called Clever Hans predictors that undermine robustness even in large-scale foundation and self-supervised models. Group distributional robustness methods, such as Deep Feature Reweighting (DFR) rely on explicit group labels to upweight underrepresented subgroups, but face key limitations: (1) group labels are often unavailable, (2) low within-group sample sizes hinder coverage of the subgroup distribution, and (3) performance degrades sharply when multiple spurious correlations fragment the data into even smaller groups. We propose Counterfactual Knowledge Distillation (CFKD), a framework that sidesteps these issues by generating diverse counterfactuals, enabling a human annotator to efficiently explore and correct the model's decision boundaries through a knowledge distillation step. Unlike DFR, our method not only reweights the undersampled groups, but it also enriches them with new data points. Our method does not require any confounder labels, achieves effective scaling to multiple confounders, and yields balanced generalization across groups. We demonstrate CFKD's efficacy across five datasets, spanning synthetic tasks to an industrial application, with particularly strong gains in low-data regimes with pronounced spurious correlations. Additionally, we provide an ablation study on the effect of the chosen counterfactual explainer and teacher model, highlighting their impact on robustness.