  This paper presents an anomaly detection model that combines the strong
statistical foundation of density-estimation-based anomaly detection methods
with the representation-learning ability of deep-learning models. The method
combines an autoencoder, for learning a low-dimensional representation of the
data, with a density-estimation model based on random Fourier features and
density matrices in an end-to-end architecture that can be trained using
gradient-based optimization techniques. The method predicts a degree of
normality for new samples based on the estimated density. A systematic
experimental evaluation was performed on different benchmark datasets. The
experimental results show that the method performs on par with or outperforms
other state-of-the-art methods.
