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

Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry Vetrov
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1308-1317, 2020.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v124-lyzhov20a, title = {Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation}, author = {Lyzhov, Alexander and Molchanova, Yuliya and Ashukha, Arsenii and Molchanov, Dmitry and Vetrov, Dmitry}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {1308--1317}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/lyzhov20a/lyzhov20a.pdf}, url = { http://proceedings.mlr.press/v124/lyzhov20a.html }, 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.} }
Endnote
%0 Conference Paper %T Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation %A Alexander Lyzhov %A Yuliya Molchanova %A Arsenii Ashukha %A Dmitry Molchanov %A Dmitry Vetrov %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-lyzhov20a %I PMLR %P 1308--1317 %U http://proceedings.mlr.press/v124/lyzhov20a.html %V 124 %X 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.
APA
Lyzhov, A., Molchanova, Y., Ashukha, A., Molchanov, D. & Vetrov, D.. (2020). Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:1308-1317 Available from http://proceedings.mlr.press/v124/lyzhov20a.html .

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