Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images
Marius Giger,
André Csillaghy
Abstract:Deep learning is successful in many fields due to its ability to learn strong feature representations without the need for hand‐crafted features, resulting in models with high representational power. However, many of these models are based on supervised learning and therefore depend on the availability of large annotated data sets. These are often difficult to obtain because they require human input. A common challenge for researchers in space weather is the sparsity of annotations in many of the available dat… Show more
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