  In novelty detection, the goal is to decide if a new data point should be
categorized as an inlier or an outlier, given a training dataset that primarily
captures the inlier distribution. Recent approaches typically use deep encoder
and decoder network frameworks to derive a reconstruction error, and employ
this error either to determine a novelty score, or as the basis for a one-class
classifier. In this research, we use a similar framework but with a lightweight
deep network, and we adopt a probabilistic score with reconstruction error. Our
methodology calculates the probability of whether the sample comes from the
inlier distribution or not. This work makes two key contributions. The first is
that we compute the novelty probability by linearizing the manifold that holds
the structure of the inlier distribution. This allows us to interpret how the
probability is distributed and can be determined in relation to the local
coordinates of the manifold tangent space. The second contribution is that we
improve the training protocol for the network. Our results indicate that our
approach is effective at learning the target class, and it outperforms recent
state-of-the-art methods on several benchmark datasets.
