Low Compute Unlearning via Sparse Representations

Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Curtis Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal

Keywords: Sparse Representations, Discrete Bottlenecks, Model Editing, Unlearning

TL;DR: We propose a low compute unlearning solution for neural networks which involves use of a specific type of discrete bottlenecks in the model architecture

Abstract:

Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be
costly and infeasible using existing techniques. We propose a low compute 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 *class unlearning* using four datasets: CIFAR-10, CIFAR-100, LACUNA-100 and ImageNet-1k. 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 minimal computational cost.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 9748