Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity. Despite its importance in different application settings, designing a novelty detector is utterly complex due to the unpredictable nature of novelties and its inaccessibility during the training procedure, factors which expose the unsupervised nature of the problem. In our proposal, we design a general framework where we equip a deep autoencoder with a parametric density estimator that learns the probability distribution underlying its latent representations through an autoregressive procedure. We show that a maximum likelihood objective, optimized in conjunction with the reconstruction of normal samples, effectively acts as a regularizer for the task at hand, by minimizing the differential entropy of the distribution spanned by latent vectors. In addition to providing a very general formulation, extensive experiments of our model on publicly available datasets deliver on-par or superior performances if compared to state-of-the-art methods in one-class and video anomaly detection settings. Differently from prior works, our proposal does not make any assumption about the nature of the novelties, making our work readily applicable to diverse contexts.
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor
Graduate School of Science and Technology, University of Tsukuba（科技研究生学校，茨口大学）；Institute of Systems and Information Engineering, University of Tsukuba（系统与信息工程研究所，茨口大学）；Center for Artificial Intelligence Research, University of Tsukuba（人工智能研究中心，茨口大学）；Division of Physics, Faculty of Pure and Applied Sciences, University of Tsukuba（物理系，应用科学 faculty，茨口大学）；Tomonaga Center for the History of the Universe (TCHoU), Faculty of Pure and Applied Sciences, University of Tsukuba（汤本纳宇宙历史中心（TCHoU），应用科学 faculty，茨口大学）
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning
Fu Jen Catholic University（辅仁大学）；National Tsing Hua University（清华大学）；National Taiwan University of Science and Technology（台湾科技大学）；United Microelectronics Corporation（联华电子股份有限公司）
