Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning

(1) Background: Machine unlearning plays a crucial role in privacy protection and model optimization, particularly in forgetting entire categories of data in classification tasks. However, existing methods often struggle with high computational costs, such as estimating the inverse Hessian, or require access to the original training data, limiting their practicality. (2) Methods: In this work, we introduce Scrub-and-Learn, which is a category-aware weight modification framework designed to remove class-level knowledge efficiently. By modeling unlearning as a continual learning task, our method leverages re-encoded labels of samples from the target category to guide weight updates, effectively scrubbing unwanted knowledge while preserving the rest of the model’s capacity. (3) Results and Conclusions: Experimental results on multiple benchmarks demonstrate that our method effectively eliminates targeted categories—achieving a recognition rate below 5%—while preserving the performance of retained classes within a 4% deviation from the original model.