Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen
  Classification from Microscopic Images
Nam Cao Olga Saukh
Distribution shifts are characterized by differences between the training and
test data distributions. They can significantly reduce the accuracy of machine
learning models deployed in real-world scenarios. This paper explores the
distribution shift problem when classifying pollen grains from microscopic
images collected in the wild with a low-cost camera sensor. 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. In particular, we show that
Tenengrad and ImageToSketch filters are highly effective to balance the shape
and texture information while leaving out unimportant details that may confuse
the model. Extensive evaluations on various model architectures demonstrate a
consistent improvement of the model generalization to field data of up to 14%
achieved by the geometric augmentation techniques when compared to a wide range
of standard image augmentations. The approach is validated through an ablation
study using pollen hydration tests to recover the shape of dry pollen grains.
The proposed geometric augmentations also receive the highest scores according
to the affinity and diversity measures from the literature.