提出ISPF方法，解决无数据情况下的高效遗忘问题
Toward Efficient Data-Free Unlearning
- 通过抑制合成数据中的遗忘信息，减少过滤导致的知识丢失
- 利用后过滤机制充分保留合成样本中的保留知识
- 在不依赖真实数据的情况下实现更优的遗忘性能
在无法访问真实数据分布的情况下实现机器遗忘极具挑战性。现有基于无数据蒸馏的方法通过过滤包含遗忘信息的合成样本实现遗忘，但难以高效蒸馏保留相关知识。本文分析发现，该问题源于过度过滤导致合成保留信息减少。为此，提出新型方法抑制性合成后过滤（ISPF），从两方面改进：一是抑制合成中遗忘信息的生成；二是通过后过滤充分利用合成样本中的保留信息。实验表明，所提ISPF有效应对该挑战，性能优于现有方法。
原文摘要 · Abstract (English)
Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic samples containing forgetting information but struggled to distill the retaining-related knowledge efficiently. In this work, we analyze that such a problem is due to over-filtering, which reduces the synthesized retaining-related information. We propose a novel method, Inhibited Synthetic PostFilter (ISPF), to tackle this challenge from two perspectives: First, the Inhibited Synthetic, by reducing the synthesized forgetting information; Second, the PostFilter, by fully utilizing the retaining-related information in synthesized samples. Experimental results demonstrate that the proposed ISPF effectively tackles the challenge and outperforms existing methods.
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