Cited by 796Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningMachine Learning and Data Classification

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Improving Deep Learning with Generic Data Augmentation

Taylor, Luke, Nitschke, Geoff·2018·2018 IEEE Symposium Series on Computational Intelligence (SSCI)

DOI 10.1109/ssci.2018.8628742

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Abstract

Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious.Data augmentationovercomes this issue by artificially inflating the training set with label preserving transformations. Recently there has been extensive use of generic data augmentation to improveConvolutional Neural Network(CNN) task performance. This study benchmarks various popular data augmentation schemes to allow researchers to make informed decisions as to which training methods are most appropriate for their data sets. Various geometric and photometric schemes are evaluated on a coarse-grained data set using a relatively simple CNN. Experimental results, run using 4-fold cross-validation and reported in terms of Top-1 and Top-5 accuracy, indicate that croppingin geometric augmentationsignificantly increases CNN task performance.

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Authors: Taylor, L., & Nitschke, G.
Affiliations: University of Cape Town (ZA)
ORCID: Taylor, Luke
Nitschke, Geoff
Venue: 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Publisher: IEEE
Published: 2018-11
Pages: 1542-1547
DOI: 10.1109/ssci.2018.8628742
OpenAlex: W2963459241
Cited by: 796
References: 33
Citations by year: 2026: 52 · 2025: 128 · 2024: 152 · 2023: 116 · 2022: 122 · 2021: 106 · 2020: 67 · 2019: 39 · 2018: 13
FWCI: 16.77
Type: conference paper
Language: en
Open access: closed
Locations: IEEE Symposium Series on Computational Intelligence (SSCI) (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords: data augmentation, convolutional neural networks, geometric augmentation, cropping, limited training data, image classification

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Improving Deep Learning with Generic Data Augmentation

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