Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
arXiv:2605.22871v1 Announce Type: new 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. Moreover, they can undermine the original learning objective and typically do not guarantee equivalence to standard unlearning by retraining. In this paper, we propose \textbf{ManiF-SMC} (\textbf{Mani}fold \textbf{F}orgetting with \textbf{S}elf \textbf{M}ode \textbf{C}onnectivity), motivated by the observation that a model retrai
The increasing focus on data privacy regulations globally, such as 'the right to be forgotten,' is driving the need for effective machine unlearning solutions.
This development improves the technical feasibility and reliability of complying with data privacy mandates, especially for large AI models whose training data can be vast and sensitive.
Machine unlearning techniques are becoming more robust and closer to the ideal of retraining equivalence, moving beyond less effective methods like label manipulation.
- · AI/ML developers
- · Cloud service providers
- · Data privacy advocates
- · Legal departments
- · Organizations with poor data governance
- · Ineffective unlearning solution providers
AI models can be more easily updated to remove specific data points without full retraining, saving compute and time.
Increased trust in AI systems due to stronger guarantees regarding privacy and data removal capabilities.
New regulatory standards for machine learning models might emerge, requiring explicit unlearning capabilities to be demonstrated.
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