  Test-time data augmentation$-$averaging the predictions of a machine learning
model across multiple augmented samples of data$-$is a widely used technique
that improves the predictive performance. While many advanced learnable data
augmentation techniques have emerged in recent years, they are focused on the
training phase. Such techniques are not necessarily optimal for test-time
augmentation and can be outperformed by a policy consisting of simple crops and
flips. The primary goal of this paper is to demonstrate that test-time
augmentation policies can be successfully learned too. We introduce greedy
policy search (GPS), a simple but high-performing method for learning a policy
of test-time augmentation. We demonstrate that augmentation policies learned
with GPS achieve superior predictive performance on image classification
problems, provide better in-domain uncertainty estimation, and improve the
robustness to domain shift.
