AtmoDist: Self-supervised representation learning for atmospheric dynamics
Representation learning has proven to be Curling - Accessories - Miscellaneous a powerful methodology in a wide variety of machine-learning applications.For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training.In this work, we show how to sidestep