# KeepAugment: A Simple Information-Preserving Data Augmentation Approach

Source: [KeepAugment: A Simple Information-Preserving Data Augmentation Approach](<https://www.sota2.com/research/paper/68fa68c31e209131cea1e272>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:45:52.748Z

Authors: Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu

Published: 2020-11-23T22:43:04.000Z

## Abstract

Data augmentation (DA) is an essential technique for training state-of-the-art deep learning systems. In this paper, we empirically show data augmentation might introduce noisy augmented examples and consequently hurt the performance on unaugmented data during inference. To alleviate this issue, we propose a simple yet highly effective approach, dubbed \\emph{KeepAugment}, to increase augmented images fidelity. The idea is first to use the saliency map to detect important regions on the original images and then preserve these informative regions during augmentation. This information-preserving strategy allows us to generate more faithful training examples. Empirically, we demonstrate our method significantly improves on a number of prior art data augmentation schemes, e.g. AutoAugment, Cutout, random erasing, achieving promising results on image classification, semi-supervised image classification, multi-view multi-camera tracking and object detection.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.11778>)

[PDF](<https://arxiv.org/pdf/2011.11778.pdf>)