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

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

URL: http://arxiv.org/abs/2002.09103v2
Date: Sat, 20 Jun 2020 13:10:23 GMT
Title: Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Authors: Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Vetrov
Abstract summary: We introduce greedy policy search (GPS) as 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.
Score: 65.92151529708036
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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