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

• URL: http://arxiv.org/abs/2312.08785v2
• Date: Fri, 5 Jan 2024 11:42:22 GMT
• Title: Managing the unknown: a survey on Open Set Recognition and tangential areas
• Authors: Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas, Javier Del Ser
• Abstract summary: Open Set Recognition models are 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. Our work also uncovers open problems and suggests several research directions that may motivate and articulate future efforts towards more safe Artificial Intelligence methods.
• Score: 7.345136916791223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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