Forget or Fine-tune? A Comparative Study of Machine Unlearning Strategies for Noisy Label Correction

arXiv:2608.30046 · cs.LG, cs.AI · Submitted 2026-08-30 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-30

Updated: 2026-08-30

Terminology

• Accuracy — 23× in this paper · explained in 1 episode
• Machine Unlearning — 20× in this paper · explained in 2 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Random Labeling — 4× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Scaled Activation Projection — 2× in this paper
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Machine Unlearning of Features and Labels
• SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

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