Learning by Self-Explaining (LSX): A Novel Approach to Enhancing AI Generalization and Faithful Model Explanations through Self-Refinement

Explainable AI (XAI) has emerged as a critical field, focusing on providing interpretable insights into machine learning model decisions. Self-explaining models, utilizing techniques such as backpropagation-based, model distillation, and prototype-based approaches, aim to elucidate decision-making processes. However, most existing studies treat explanations as one-way communication tools for model inspection, neglecting their potential to actively contribute […]

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