Model Guidance via Robust Feature Attribution
The paper introduces R4, a method that simultaneously optimizes for explanation robustness and reduces shortcut learning in machine learning models. It addresses the unreliability of traditional feature attribution by creating an objective function that ensures irrelevant features have small and stable gradients. Experiments show R4 consistently reduces test-time misclassifications by 20% compared to state-of-the-art methods across various domains.