EntUn: Mitigating the forget-retain dilemma in unlearning via entropy
Dahuin Jung · ICT Express · 2025
Advancements in natural language processing and computer vision have raised concerns about models inadvertently exposing private data and confidently misclassifying inputs. Machine unlearning has emerged as a solution, enabling the removal of specific data influences to meet privacy standards. This work focuses on unlearning in Instance-Removal (IR) and Class-Removal (CR) scenarios: IR targets the removal of individual data points, while CR eliminates all data related to a specific class. We propose EntUn , which maximizes entropy for the forget-set to reduce confidence in data to be forgotten and minimizes it for the retain-set to preserve discriminative power. An entropy-based intra-class mixup further stabilizes this process, using higher-entropy samples to guide controlled information removal. Experiments on CIFAR10, CIFAR100, and TinyImageNet show that EntUn outperforms state-of-the-art baselines, improving forgetting and enhancing privacy protection as confirmed by membership inference attack tests. This demonstrates entropy maximization as a robust strategy for effective unlearning.