One-Class Classification: A Survey
This survey provides a comprehensive overview of OCC methods, datasets, and metrics for researchers and practitioners working on anomaly detection and novelty detection problems.
This paper surveys classical statistical and recent deep learning-based methods for One-Class Classification (OCC), where training data comes from a single positive class and the goal is to recognize positive queries during inference. It discusses the merits and drawbacks of existing approaches, identifies promising research avenues, and presents commonly used datasets and evaluation metrics.
One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class. The goal of OCC is to learn a representation and/or a classifier that enables recognition of positively labeled queries during inference. This topic has received considerable amount of interest in the computer vision, machine learning and biometrics communities in recent years. In this article, we provide a survey of classical statistical and recent deep learning-based OCC methods for visual recognition. We discuss the merits and drawbacks of existing OCC approaches and identify promising avenues for research in this field. In addition, we present a discussion of commonly used datasets and evaluation metrics for OCC.