A Contrastive Learning Approach to Auroral Identification and Classification

@article{Johnson2021ACL,
  title={A Contrastive Learning Approach to Auroral Identification and Classification},
  author={Jeremiah W. Johnson and Swathi Hari and Donald L. Hampton and Hyunju K. Connor and Amy M Keesee},
  journal={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2021},
  pages={772-777}
}
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… 

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