  Most state-of-the-art computer vision models heavily depend on data. However,
many datasets exhibit extreme class imbalance which has been shown to
negatively impact model performance. Among the training-time and
data-generation solutions that have been explored, one subset that leverages
existing data is importance sampling. A good deal of this work focuses
primarily on the CIFAR-10 and CIFAR-100 datasets which fail to be
representative of the scale, composition, and complexity of current
state-of-the-art datasets. In this work, we explore and compare three
techniques that derive from importance sampling: loss reweighting,
undersampling, and oversampling. Specifically, we compare the effect of these
techniques on the performance of two encoders on an impactful satellite imagery
dataset, Planet's Amazon Rainforest dataset, in preparation for another work.
Furthermore, we perform supplemental experimentation on a scene classification
dataset, ADE20K, to test on a contrasting domain and clarify our results.
Across both types of encoders, we find that up-weighting the loss for and
undersampling has a negigible effect on the performance on underrepresented
classes. Additionally, our results suggest oversampling generally improves
performance for the same underrepresented classes. Interestingly, our findings
also indicate that there may exist some redundancy in data in the Planet
dataset. Our work aims to provide a foundation for further work on the Planet
dataset and similar domain-specific datasets. We open-source our code at
https://github.com/RichardZhu123/514-class-imbalance for future work on other
satellite imagery datasets as well.
