arXiv cs.LGPaper
The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
This is careful scientific hygiene. If you're evaluating unlearning methods on vision models with BatchNorm, a forward pass on retain data alone can fake forgetting, and this paper formalizes exactly what's happening and how to separate measurement noise from real encoder failure. The immediate takeaway: unlearning papers using BN architectures need this check in their evaluation pipeline, or their claimed performance gains may be illusory.