Paper recorded by Signals 4 on 2026-10-27 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28
Category: cs.LG · 机器学习 · first seen 2026-10-28
Test-time adaptation helps a deployed model respond to changing inputs, but the same intervention can rescue one batch and damage the next. Updating normalization statistics, for example, can improve corrupted images yet fail when a batch contains mostly one class. Average accuracy hides these failures and cannot tell a model when to use an intervention. Target labels can guide this decision, but