Managing the unknown: a survey on Open Set Recognition and tangential areas

Autor:	Barcina-Blanco, Marcos, Lobo, Jesus L., Garcia-Bringas, Pablo, Del Ser, Javier
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition A.1 I.5.0
Druh dokumentu:	Working Paper
Popis:	In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage. Open Set Recognition addresses this issue by devising models capable of detecting unknown classes from samples arriving during the testing phase, while maintaining a good level of performance in the classification of samples belonging to known classes. This review comprehensively overviews the recent literature related to Open Set Recognition, identifying common practices, limitations, and connections of this field with other machine learning research areas, such as continual learning, out-of-distribution detection, novelty detection, and uncertainty estimation. Our work also uncovers open problems and suggests several research directions that may motivate and articulate future efforts towards more safe Artificial Intelligence methods. Comment: 35 pages, 1 figure, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.08785 Zobrazit plný text záznamu View this record from Arxiv