Deep Variational Semi-Supervised Novelty Detection

ICLR 2020 Tal DanielThanard KurutachAviv Tamar

In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generative models, such as variational autoencoders (VAEs), for unsupervised learning of the normal data distribution... (read more)

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