Neural networks trained via supervised contrastive learning yield feature attributions that are more faithful, less complex, and more continuous than those from cross-entropy trained networks.
In: The Thirteenth International Conference on Learning Representations (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
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A cosine-similarity metric on SHAP feature attributions is proposed to quantify explanation stability for same-label inputs under perturbations in transformer-based sentiment classifiers.
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Empirical Characterization of Rationale Stability Under Controlled Perturbations for Explainable Pattern Recognition
A cosine-similarity metric on SHAP feature attributions is proposed to quantify explanation stability for same-label inputs under perturbations in transformer-based sentiment classifiers.