  Novelty detection is the problem of identifying whether a new data point is
considered to be an inlier or an outlier. We assume that training data is
available to describe only the inlier distribution. Recent approaches primarily
leverage deep encoder-decoder network architectures to compute a reconstruction
error that is used to either compute a novelty score or to train a one-class
classifier. While we too leverage a novel network of that kind, we take a
probabilistic approach and effectively compute how likely is that a sample was
generated by the inlier distribution. We achieve this with two main
contributions. First, we make the computation of the novelty probability
feasible because we linearize the parameterized manifold capturing the
underlying structure of the inlier distribution, and show how the probability
factorizes and can be computed with respect to local coordinates of the
manifold tangent space. Second, we improved the training of the autoencoder
network. An extensive set of results show that the approach achieves
state-of-the-art results on several benchmark datasets.
