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In real-world scenarios classification models are often required to performrobustly when predicting samples belonging to classes that have not appearedduring its training stage. Open Set Recognition addresses this issue bydevising models capable of detecting unknown classes from samples arrivingduring the testing phase, while maintaining a good level of performance in theclassification of samples belonging to known classes. This reviewcomprehensively overviews the recent literature related to Open SetRecognition, identifying common practices, limitations, and connections of thisfield 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 directionsthat may motivate and articulate future efforts towards more safe ArtificialIntelligence methods.

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