Exploring Automated Data Augmentation Approaches for Deep Learning: A Case Study of Individual Feral Cat Classification

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dc.contributor.authorYang, Zihan
dc.contributor.authorSinnott, Richard
dc.contributor.authorBailey, James
dc.contributor.authorEhinger, Krista A.
dc.date.accessioned2023-12-26T18:36:46Z
dc.date.available2023-12-26T18:36:46Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.140
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other9e14ecac-5537-4b8c-bc8d-29dc1535b4e6
dc.identifier.urihttps://hdl.handle.net/10125/106517
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Intelligence and Visual Analytics
dc.subjectautoda
dc.subjectcomputer vision
dc.subjectdata augmentation
dc.subjectdeep learning
dc.subjecthyperparameter tuning
dc.titleExploring Automated Data Augmentation Approaches for Deep Learning: A Case Study of Individual Feral Cat Classification
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThis paper evaluates the performance of several automated data augmentation (AutoDA) methods for image classification problems suited for scenarios with limited and potentially imbalanced data sets. We compare one-stage, two-stage and search-free methods. These are explored in the context of a case study to identify/count feral cats in rural Victoria. Our results show that a trade-off exists between accuracy and efficiency, with one-stage methods being faster but less accurate than two-stage methods. Search-free methods are fastest, but have limited improvement in the resultant classification accuracy.
dcterms.extent10 pages
prism.startingpage1159

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