Machine Unlearning Comparison Hub
3 papers - avg viability 5.7
Top Papers
- Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models(7.0)
A novel method for prompt-free instance unlearning in diffusion models to enhance privacy and ethical compliance.
- FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning(6.0)
Develop a forgetting-aware loss reweighting method to improve machine unlearning for long-tailed user data.
- Reference-Guided Machine Unlearning(4.0)
Reference-Guided Unlearning (ReGUn) enhances machine unlearning by ensuring models forget specific data while maintaining performance.