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

Date
2024-01-03
Authors
Yang, Zihan
Sinnott, Richard
Bailey, James
Ehinger, Krista A.
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1159
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Abstract
This 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.
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Decision Intelligence and Visual Analytics, autoda, computer vision, data augmentation, deep learning, hyperparameter tuning
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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