Multi-Class Unlearning for Image Classification via Weight Filtering

TL;DR

This paper introduces a novel multi-class unlearning framework for image classification that uses weight filtering and memory matrices, enabling selective class removal and explainability in neural networks.

Contribution

It presents a new method for unlearning all classes simultaneously using weight filtering, which is more comprehensive than existing class-specific approaches.

Findings

Effective unlearning of all classes in a single round

Applicable to convolutional and Transformer architectures

Provides explainable class representations

Abstract

Machine Unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network. Unlike existing methods that target a limited subset or a single class, our framework unlearns all classes in a single round. We achieve this by modulating the network's components using memory matrices, enabling the network to demonstrate selective unlearning behavior for any class after training. By discovering weights that are specific to each class, our approach also recovers a representation of the classes which is explainable by design. We test the proposed framework on small- and medium-scale image classification datasets, with both convolution- and Transformer-based backbones, showcasing the potential for explainable solutions through unlearning.