Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose a nearly compute-free zero-shot unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the data set. We evaluate the proposed technique on the problem of \textit{class unlearning} using three datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all three datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.
@article{arxiv.2311.15268,
title = {Unlearning via Sparse Representations},
author = {Vedant Shah and Frederik Träuble and Ashish Malik and Hugo Larochelle and Michael Mozer and Sanjeev Arora and Yoshua Bengio and Anirudh Goyal},
journal= {arXiv preprint arXiv:2311.15268},
year = {2024}
}