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

The DOI matches its Crossref record; 30 of the 33 references it cites were found in a registry or an index.

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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  1. nesterov, 1983, A method of solving a convex programming problem with convergence rate O(1/k2), Soviet Mathematics Doklady, 27, 372

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Metadata

Authors
Taylor, L., & Nitschke, G.
Affiliations
University of Cape Town (ZA)
ORCID
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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