Search Results for author:
Found 1 papers, 0 papers with code
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
We leverage the domain knowledge that geometric features are highly important for accurate pollen identification and introduce two novel geometric image augmentation techniques to significantly narrow the accuracy gap between the model performance on the train and test datasets.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
