Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

Understand

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.