This example compares a baseline without adaptation (base, or source), test-time normalization that updates feature statistics during testing (norm), and our method for entropy minimization during testing (tent). Each experiment measures accuracy (%) on corrupted data. Clean accuracy is 94.78%. - dataset: CIFAR-10-C (https://github.com/hendrycks/robustness/), with 15 corruption types and 5 levels - model: WRN-28-10, the default for RobustBench (https://github.com/RobustBench/robustbench)