Shapley-Guided Neural Repair Approach via Derivative-Free Optimization
DNNs are susceptible to defects like backdoors, adversarial attacks, and unfairness, undermining their reliability. Existing approaches mainly involve retraining, optimization, constraint-solving, or search algorithms. However, most methods rely on gradient calculations, restricting applicability to specific activation functions (e.g., ReLU), or use search algorithms with uninterpretable localization and repair. Furthermore, they often lack generalizability across multiple properties. We propose SHARPEN, integrating interpretable fault localization with a derivative-free optimization strategy. First, SHARPEN introduces a Deep SHAP-based localization strategy quantifying each layer's and n...
| Authors | Xinyu Sun, Wanwei Liu, Haoang Chi et al. |
|---|---|
| Primary category | cs.SE |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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