Efficient Learning on Successive Test Time Augmentation

Abstract

Test time augmentation (TTA) has been a promising tool for improving the robustness against out-of-distribution data at inference time. Recent TTA methods try to learn predictive transformations which are supposed to provide the best performance gain on each test sample. However, existing methods are either restricted to predicting one single transformation for each sample or require multiple forward passes of the transformation predictor, leading to a sub-optimal solution regarding efficiency. In this paper, we propose a novel method to predict successive test time augmentations. For the first time, it only requires a single forward pass of the transformation predictor, while can output multiple desired transformations iteratively. The experimental results show that our method provides a significant and consistent improvement in model robustness against various corruptions while significantly surpassing state-of-the-arts in runtime.