AND: Autoregressive Novelty Detectors

We propose an unsupervised model for novelty detection. The subject is treated as a density estimation problem, in which a deep neural network is employed to learn a parametric function that maximizes probabilities of training samples. This is achieved by equipping an autoencoder with a novel module, responsible for the maximization of compressed codes' likelihood by means of autoregression. We illustrate design choices and proper layers to perform autoregressive density estimation when dealing with both image and video inputs. Despite a very general formulation, our model shows promising results in diverse one-class novelty detection and video anomaly detection benchmarks.