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

Noisy labels remain a critical challenge for training deep neural networks, since memorizing incorrect labels degrades generalization. Once noisy samples are identified after training, the standard solution is to retrain the model from scratch on the cleaned dataset, which is increasingly expensive