  Novelty detection is commonly referred to as the discrimination of
observations that do not conform to a learned model of regularity. Despite its
importance in different application settings, designing a novelty detector is
utterly complex due to the unpredictable nature of novelties and its
inaccessibility during the training procedure, factors which expose the
unsupervised nature of the problem. In our proposal, we design a general
framework where we equip a deep autoencoder with a parametric density estimator
that learns the probability distribution underlying its latent representations
through an autoregressive procedure. We show that a maximum likelihood
objective, optimized in conjunction with the reconstruction of normal samples,
effectively acts as a regularizer for the task at hand, by minimizing the
differential entropy of the distribution spanned by latent vectors. In addition
to providing a very general formulation, extensive experiments of our model on
publicly available datasets deliver on-par or superior performances if compared
to state-of-the-art methods in one-class and video anomaly detection settings.
Differently from prior works, our proposal does not make any assumption about
the nature of the novelties, making our work readily applicable to diverse
contexts.
