Anomaly Detection With Multiple-Hypotheses Predictions

ICLR 2019 Duc Tam NguyenZhongyu LouMichael KlarThomas Brox

In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples. Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the foreground, are used... (read more)

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