Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

18 Jun 2020Robin Tibor SchirrmeisterYuxuan ZhouTonio BallDan Zhang

Deep generative networks trained via maximum likelihood on a natural image dataset like CIFAR10 often assign high likelihoods to images from datasets with different objects (e.g., SVHN). We refine previous investigations of this failure at anomaly detection for invertible generative networks and provide a clear explanation of it as a combination of model bias and domain prior: Convolutional networks learn similar low-level feature distributions when trained on any natural image dataset and these low-level features dominate the likelihood... (read more)

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