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.

该论文研究了在野外使用低成本相机传感器对显微图像中的花粉粒子进行分类时的分布漂移问题，提出了两种新的几何图像增强技术，通过平衡形状和纹理信息并去除干扰细节，显著缩小了训练集和测试集上模型性能的准确性差距，经过多种模型架构的广泛评估，这些几何增强技术相对于一系列标准图像增强可提高模型对野外数据的泛化能力最高达 14%。