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.

翻译：在无法访问真实数据分布的情况下实现机器遗忘学习具有挑战性。现有的基于无数据蒸馏的方法通过过滤掉包含遗忘信息的合成样本实现了遗忘，但难以高效地蒸馏出与保留相关的知识。在本工作中，我们分析认为该问题源于过度过滤，这减少了合成的与保留相关的信息。我们提出了一种新方法——抑制合成后过滤器（ISPF），从两个角度应对这一挑战：第一，通过减少合成的遗忘信息来实现"抑制合成"；第二，通过充分利用合成样本中与保留相关的信息来实现"后过滤"。实验结果表明，所提出的ISPF方法有效地应对了这一挑战，并优于现有方法。