Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks
Germán García-Jara,
Pavlos Protopapas,
Pablo A. Estévez
Abstract:Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. Currently, classification consists of supervised machine learning algorithms whose performance is limited by the number of existing annotations of astronomical objects and their highly imbalanced class distributions. In this work, we propose a data augmentation methodology based on Generative Adversarial Networks (GANs)… Show more
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