# A Preliminary Study on Data Augmentation of Deep Learning for Image Classification

Source: [A Preliminary Study on Data Augmentation of Deep Learning for Image Classification](<https://www.sota2.com/research/paper/68fa65f31e209131cea0d585>)

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

Data updated: 2025-10-25T21:39:29.972Z

Authors: Benlin Hu, Cheng Lei, Dong Wang, Shu Zhang, Zhenyu Chen

Published: 2019-06-09T07:21:09.000Z

## Abstract

Deep learning models have a large number of freeparameters that need to be calculated by effective trainingof the models on a great deal of training data to improvetheir generalization performance. However, data obtaining andlabeling is expensive in practice. Data augmentation is one of themethods to alleviate this problem. In this paper, we conduct apreliminary study on how three variables (augmentation method,augmentation rate and size of basic dataset per label) can affectthe accuracy of deep learning for image classification. The studyprovides some guidelines: (1) it is better to use transformationsthat alter the geometry of the images rather than those justlighting and color. (2) 2-3 times augmentation rate is good enoughfor training. (3) the smaller amount of data, the more obviouscontributions could have.

## 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/1906.11887>)

[PDF](<https://arxiv.org/pdf/1906.11887.pdf>)