Out-of-Distribution (OOD) Detection Based on Deep Learning: A Review
Peng Cui, Jinjia Wang
Abstract
Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep learning is the most studied in OOD detection. In this paper, related basic information on OOD detection based on deep learning is described, and we categorize methods according to the training data. OOD detection is divided into supervised, semisupervised, and unsupervised. Where supervised data are used, the methods are categorized according to technical means: model-based, distance-based, and density-based. Each classification is introduced with background, examples, and applications. In addition, we present the latest applications of OOD detection based on deep learning and the problems and expectations in this field.