Improving Deep Learning with Generic Data Augmentation
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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Data augmentation can increase the quantity and diversity of data available for training ML algorithms, improving the accuracy and reliability of results.
“One approach is the use of data augmentation [46], which involves the generation of synthetic data to supplement existing datasets. This can increase the quantity and diversity of the data available for training ML algorithms, improving the accuracy and reliability of the results [47].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [47]
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- Authors
- Taylor, L., & Nitschke, G.
- Affiliations
- University of Cape Town (ZA)
- Venue
- 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 1542-1547
- 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 · 2017: 1
- 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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