Generative Probabilistic Novelty Detection with Adversarial Autoencoders
This work addresses the problem of identifying outliers in data for applications like anomaly detection, but it is incremental as it builds on existing encoder-decoder architectures with probabilistic enhancements.
The paper tackles novelty detection by developing a probabilistic method using adversarial autoencoders to compute the likelihood that a sample belongs to the inlier distribution, achieving state-of-the-art results on multiple benchmark datasets.
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.