  Inspired by recent advances in coverage-guided analysis of neural networks,
we propose a novel anomaly detection method. We show that the hidden activation
values contain information useful to distinguish between normal and anomalous
samples. Our approach combines three neural networks in a purely data-driven
end-to-end model. Based on the activation values in the target network, the
alarm network decides if the given sample is normal. Thanks to the anomaly
network, our method even works in strict semi-supervised settings. Strong
anomaly detection results are achieved on common data sets surpassing current
baseline methods. Our semi-supervised anomaly detection method allows to
inspect large amounts of data for anomalies across various applications.
