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arXiv Machine Learning By Aaryaman Kalani, Murari Mandal, Dhruv Kumar, Mohan Kankanhalli, Yash Sinha

The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation

Read the original on arXiv Machine Learning →

The paper uncovers a hidden flaw in evaluating machine unlearning on BatchNorm-based models: a single forward pass over retained data can alter the model’s normalization state without changing weights, misleadingly restoring performance. This effect, formalized as a weight‑preserving fixed‑point operator, shows that apparent forgetting can be entirely due to BatchNorm running statistics, not to the unlearning method itself. Experiments demonstrate that the artifact can inflate forget accuracy by up to 78 percentage points across nine methods, and that switching to GroupNorm eliminates the problem.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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