COME: Test-time adaption by Conservatively Minimizing Entropy
COME: Test-time adaption by Conservatively Minimizing Entropy
Machine learning models must continuously self-adjust themselves for novel data distribution in the open world. As the predominant principle, entropy minimization (EM) has been proven to be a simple yet effective cornerstone in existing test-time adaption (TTA) methods. While unfortunately its fatal limitation (i.e., overconfidence) tends to result in model …