  Deep learning approaches have shown great success in image classification
tasks and can aid greatly towards the fast and reliable classification of
pollen grain aerial imagery. However, often-times deep learning methods in the
setting of natural images can suffer generalization problems and yield poor
performance on unseen test distribution. In this work, we present and a robust
deep learning framework that can generalize well for pollen grain
aerobiological imagery classification. We develop a convolutional neural
network-based pollen grain classification approach and combine some of the best
practices in deep learning for better generalization. In addition to
commonplace approaches like data-augmentation and weight regularization, we
utilize implicit regularization methods like manifold mixup to allow learning
of smoother decision boundaries. We also make use of proven state-of-the-art
architectural choices like EfficientNet convolutional neural networks. Inspired
by the success of generative modeling with variational autoencoders, we train
models with a richer learning objective which can allow the model to focus on
the relevant parts of the image. Finally, we create an ensemble of neural
networks, for the robustness of the test set predictions. Based on our
experiments, we show improved generalization performance as measured with a
weighted F1-score with the aforementioned approaches. The proposed approach
earned a fourth-place in the final rankings in the ICPR-2020 Pollen Grain
Classification Challenge; with a 0.972578 weighted F1 score,0.950828 macro
average F1 scores, and 0.972877 recognition accuracy.
