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arXiv · 2011.11156

Better Aggregation in Test-Time Augmentation

Divya Shanmugam·Davis Blalock·Guha Balakrishnan·John Guttag
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Abstract

Test-time augmentation -- the aggregation of predictions across transformed versions of a test input -- is a common practice in image classification. Traditionally, predictions are combined using a simple average. In this paper, we present 1) experimental analyses that shed light on cases in which the simple average is suboptimal and 2) a method to address these shortcomings. A key finding is that even when test-time augmentation produces a net improvement in accuracy, it can change many correct predictions into incorrect predictions. We delve into when and why test-time augmentation changes a prediction from being correct to incorrect and vice versa. Building on these insights, we present a learning-based method for aggregating test-time augmentations. Experiments across a diverse set of models, datasets, and augmentations show that our method delivers consistent improvements over existing approaches.

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Divya Shanmugam, Davis Blalock, Guha Balakrishnan, John Guttag. 2020-11-23. Better Aggregation in Test-Time Augmentation. https://arxiv.org/abs/2011.11156

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