Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

arXiv:2403.11491v1 Announce Type: new
Abstract: Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Although recent TTA has shown promising performance, we still face two key challenges: 1) prior methods perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications; 2) while existing TTA can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution …

abstract arxiv backpropagation challenges cs.lg data distribution face key model adaptation optimization performance prior sample test training type uncertainty