A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning
This paper proposes a unified gradient-based framework for task-agnostic continual learning-unlearning that models continual learning and machine unlearning within a single KL-divergence-based optimization objective, decomposes gradient updates into interpretable components, and introduces a Hessian-informed remain-preserved manifold and the UG-CLU algorithm to balance knowledge acquisition, targeted unlearning, and stability across benchmarks.
May 21, 2025
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
This work proposes a fast-slow parameter update strategy to implicitly approximate the up-to-date salient unlearning direction, free from specific modal constraints, and adaptable across computer vision unlearning tasks, including classification and generation.
Oct 7, 2024
Towards Natural Machine Unlearning
This method substantially reduces the over-forgetting and leads to strong robustness to hyperparameters, making it a promising candidate for practical machine unlearning.
May 24, 2024
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
A novel method, namely MU-Mis (Machine Unlearning by Minimizing input sensitivity), to suppress the contribution of the forgetting data, which is the first time that a remaining-data-free method can outperform state-of-the-art unlearning methods that utilize the remaining data.
Feb 23, 2024