Computer Science > Machine Learning
[Submitted on 10 Jul 2023 (v1), last revised 18 Jul 2023 (this version, v2)]
Title:Gradient Surgery for One-shot Unlearning on Generative Model
View PDFAbstract:Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parameters. In this paper, we introduce a simple yet effective approach to remove a data influence on the deep generative model. Inspired by works in multi-task learning, we propose to manipulate gradients to regularize the interplay of influence among samples by projecting gradients onto the normal plane of the gradients to be retained. Our work is agnostic to statistics of the removal samples, outperforming existing baselines while providing theoretical analysis for the first time in unlearning a generative model.
Submission history
From: Seohui Bae [view email][v1] Mon, 10 Jul 2023 13:29:23 UTC (315 KB)
[v2] Tue, 18 Jul 2023 15:30:30 UTC (315 KB)
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