# The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Source: [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](<https://www.sota2.com/research/paper/68fa63d71e209131cea012d8>)

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

Data updated: 2025-10-25T20:51:10.308Z

Authors: Luis Perez, Jason Wang

Published: 2017-12-13T06:41:00.000Z

## Abstract

In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input images. We artificially constrain our access to data to a small subset of the ImageNet dataset, and compare each data augmentation technique in turn. One of the more successful data augmentations strategies is the traditional transformations mentioned above. We also experiment with GANs to generate images of different styles. Finally, we propose a method to allow a neural net to learn augmentations that best improve the classifier, which we call neural augmentation. We discuss the successes and shortcomings of this method on various datasets.

## 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/1712.04621>)

[PDF](<https://arxiv.org/pdf/1712.04621.pdf>)