Forget or Fine-tune? A Comparative Study of Machine Unlearning Strategies for Noisy Label Correction
This work conducts a comparative empirical study of five MU methods across symmetric, asymmetric, instance-dependent, and open-set noise on CIFAR-10, CIFAR-100, and the real-world noisy dataset Food-101N and finds that the appropriate unlearning strategy is conditioned on the noise structure.
J. L. Sant'Ana, Filipe R. Cordeiro
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