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Real-world image recognition systems often face corrupted input images, whichcause distribution shifts and degrade the performance of models. These systemsoften use a single prediction model in a central server and process images sentfrom various environments, such as cameras distributed in cities or cars. Suchsingle models face images corrupted in heterogeneous ways in test time. Thus,they require to instantly adapt to the multiple corruptions during testingrather than being re-trained at a high cost. Test-time adaptation (TTA), whichaims to adapt models without accessing the training dataset, is one of thesettings that can address this problem. Existing TTA methods indeed work wellon a single corruption. However, the adaptation ability is limited whenmultiple types of corruption occur, which is more realistic. We hypothesizethis is because the distribution shift is more complicated, and the adaptationbecomes more difficult in case of multiple corruptions. In fact, weexperimentally found that a larger distribution gap remains after TTA. Toaddress the distribution gap during testing, we propose a novel TTA methodnamed Covariance-Aware Feature alignment (CAFe). We empirically show that CAFeoutperforms prior TTA methods on image corruptions, including multiple types ofcorruptions.

Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation to Multiple Image Corruptions