arXiv (OAI Expanded)
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The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
Approximate machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. We identify a previously undocumented confound in how unlearning is evaluated on BatchNorm-based architectures: a single forward pass over retain data, an opera
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