Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
📰 ArXiv cs.AI
Learn to implement approximate machine unlearning through manifold representation forgetting guided by self-mode connectivity to enforce the right to be forgotten
Action Steps
- Implement ManiF-SMC by representing the data manifold using a neural network
- Use self-mode connectivity to guide the forgetting process
- Train the model with a forgetting loss function to minimize the impact of forgotten data
- Evaluate the unlearning effectiveness using metrics such as accuracy and forgetting rate
- Compare the results with standard unlearning by retraining to ensure equivalence
Who Needs to Know This
Machine learning engineers and researchers can benefit from this technique to improve the effectiveness of unlearning mechanisms in their models, while also preserving the original learning objective
Key Insight
💡 Manifold representation forgetting guided by self-mode connectivity can effectively enforce the right to be forgotten while preserving the original learning objective
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Approximate machine unlearning through manifold representation forgetting guided by self-mode connectivity #MachineLearning #Unlearning
Key Takeaways
Learn to implement approximate machine unlearning through manifold representation forgetting guided by self-mode connectivity to enforce the right to be forgotten
Full Article
Title: Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
Abstract:
arXiv:2605.22871v1 Announce Type: cross 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 wit
Abstract:
arXiv:2605.22871v1 Announce Type: cross 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 wit
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