Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
The paper presents a new approach to machine unlearning called ManiF-SMC, which focuses on manifold representation forgetting. This method aims to improve unlearning effectiveness while preserving the original learning objectives. The authors conducted extensive experiments demonstrating that ManiF-SMC achieves comparable results to existing methods by operating within the model's representation space.
- ▪ManiF-SMC stands for Manifold Forgetting with Self Mode Connectivity.
- ▪The approach reformulates unlearning as pushing erased samples away from their learned manifold representation.
- ▪It utilizes a margin-based triplet loss to encapsulate unlearning and representation preservation goals.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22871 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.22871 (cs) [Submitted on 20 May 2026] Title:Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity Authors:Weiqi Wang, Zhiyi Tian, Chenhan Zhang, Luoyu Chen, Shui Yu View a PDF of the paper titled Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity, by Weiqi Wang and 4 other authors View PDF HTML (experimental) Abstract:Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or task-gradient reversal often deliver limited unlearning effectiveness.
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