Beyond the Known: Adversarial Autoencoders for Novelty Detection

This research presents an adversarial autoencoder framework, BK-AAND, that effectively learns the distribution of inlier data and uses it to detect novel or outlier samples. The key contributions are computing the novelty probability by linearizing the inlier manifold and improving the training protocol for the network.