  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.

Machine Learning;Computer Vision and Pattern Recognition