efficient instance-aware test-time augmentation method resulting in significant gains over previous approaches

We would like to thank you for your thorough evaluation, helpful suggestions, and comments. Figure 1: Comparison for the same 5 Crop Figure 2: Comparison for the same GPS transforms candidates on the clean ImageNet set using on the clean ImageNet set using ResNet-Test-time Relative Clean Corrupted set Corrupted Test-set ResNet-50. We trained our loss predictor for We trained our loss predictor on the five crop areas. Compared to the 5-crop ensemble, searched GPS policies to choose ones specific Center-Crop 1 24.14 78.93 75.42 choosing one transform by our method for each test instance. A detailed comparison will be included.