Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Yang, Zihan; Sinnott, Richard; Bailey, James; and Ehinger, Krista A., "Exploring Automated Data Augmentation Approaches for Deep Learning: A Case Study of Individual Feral Cat Classification" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 2.
https://aisel.aisnet.org/hicss-57/da/visual_analytics/2
Exploring Automated Data Augmentation Approaches for Deep Learning: A Case Study of Individual Feral Cat Classification
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/da/visual_analytics/2