SMiR: Minimizing Side-Effects in Repairing Specific Misclassifications for Deep Neural Networks

Although Deep Neural Networks (DNNs) have achieved significant success across various industries, their erroneous behaviors have raised widespread concerns. In safety-critical domains where DNNs are deployed, certain types of specific misclassifications (i.e., instances of one specific class being misclassified as another) can lead to catastrophic consequences and urgently need to be corrected. However, existing repair approaches often neglect the adverse impact of general feature extraction patterns on distinguishing specific classes, resulting in limited correction of specific misclassifications. Furthermore, most approaches correct errors by directly adjusting the global decision boundary, which inevitably disturbs the well-trained boundaries and leads to significant accuracy loss. In this paper, we propose SMiR, an effective repair framework for addressing specific misclassifications with marginal accuracy loss. Rather than patching the original model, our method localizes and recovers the distorted feature dimensions to mitigate their negative impact on the distinction between specific classes. Moreover, SMiR narrows the scope of repair to the filtered suspect set, protecting other well-trained decision boundaries from disturbance. On average, our approach repairs 27% \(\sim\) 36.2% more specific misclassifications than the baselines, while introducing only 7.8% \(\sim\) 70.9% as many new errors. Besides, SMiR maintains state-of-the-art performance on safety-critical datasets.