Anomaly Detection with Autoencoders
Discover how autoencoders excel in detecting rare events by analyzing reconstruction errors to differentiate between normal and anomalous states.
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Anomaly detection is unarguably one of the best approaches in rare event detection problems. Especially, if the event is so rare that there are insufficient samples to train a classifier. Fortunately, an (extremely) rare event often appears as an anomaly in a process. They can, therefore, be detected due to their abnormality.
Petsche et al. 1996 have one of the early works in deep learning which developed anomaly detectors for rare event detection. They developed an autoassociator to detect an imminent motor failure.
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