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Found 2 papers, 2 papers with code
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrograms
We propose a new machine learning anomaly detection framework for classifying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen.
Improving Novelty Detection using the Reconstructions of Nearest Neighbours
We show that using nearest neighbours in the latent space of autoencoders (AE) significantly improves performance of semi-supervised novelty detection in both single and multi-class contexts.
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