  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. However, generative models suffer from a large input
dimensionality (as in images) and are typically inefficient learners. We
propose to learn the data distribution of the foreground more efficiently with
a multi-hypotheses autoencoder. Moreover, the model is criticized by a
discriminator, which prevents artificial data modes not supported by data, and
enforces diversity across hypotheses. Our multiple-hypothesesbased anomaly
detection framework allows the reliable identification of out-of-distribution
samples. For anomaly detection on CIFAR-10, it yields up to 3.9% points
improvement over previously reported results. On a real anomaly detection task,
the approach reduces the error of the baseline models from 6.8% to 1.5%.
