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Enhancing Electron Microscopy Image Classification Using Data Augmentation
DescriptionManual labeling for ML tasks is labour intensive, resulting in scarce and limited datasets. Despite the promising evidence of data augmentation, little work investigates the impact and limitations of combinatorial data augmentation. Our work addresses this gap by examining how standard and combinatorial data augmentation of small datasets affects models’ label classification performance. We generate datasets augmented with one, two, and four simultaneous augmentation techniques and compare their impact on the performance of three image classification models (DenseNet169, MobileNetV2, ResNet101V2) Our experiments show a non-monotonic relationship between augmentation quantity and classification performance. Our findings suggest the optimal augmentation quantity depends on domain and use case. We also find that the application order of augmentation techniques impacts model performance, up to 2.6% in our use case. Our work highlights the limits of data augmentation on small datasets.
Event Type
Workshop
TimeMonday, 18 November 20244:10pm - 4:30pm EST
LocationB313
Tags
Artificial Intelligence/Machine Learning
Registration Categories
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