Unsupervised learning algorithms are beginning to achieve accuracies
comparable to their supervised counterparts on benchmark computer vision tasks,
but their utility for practical applications has not yet been demonstrated. In
this work, we present a novel application of unsupervised learning to the task
of auroral image classification. Specifically, we modify and adapt the Simple
framework for Contrastive Learning of Representations (SimCLR) algorithm to
learn representations of auroral images in a recently released auroral image
dataset constructed using image data from Time History of Events and Macroscale
Interactions during Substorms (THEMIS) all-sky imagers. We demonstrate that (a)
simple linear classifiers fit to the learned representations of the images
achieve state-of-the-art classification performance, improving the
classification accuracy by almost 10 percentage points over the current
benchmark; and (b) the learned representations naturally cluster into more
clusters than exist manually assigned categories, suggesting that existing
categorizations are overly coarse and may obscure important connections between
auroral types, near-earth solar wind conditions, and geomagnetic disturbances
at the earth's surface. Moreover, our model is much lighter than the previous
benchmark on this dataset, requiring in the area of fewer than 25\% of the
number of parameters. Our approach exceeds an established threshold for
operational purposes, demonstrating readiness for deployment and utilization.
