  Open Set Recognition (OSR) is about dealing with unknown situations that were
not learned by the models during training. In this paper, we provide a survey
of existing works about OSR and distinguish their respective advantages and
disadvantages to help out new researchers interested in the subject. The
categorization of OSR models is provided along with an extensive summary of
recent progress. Additionally, the relationships between OSR and its related
tasks including multi-class classification and novelty detection are analyzed.
It is concluded that OSR can appropriately deal with unknown instances in the
real-world where capturing all possible classes in the training data is not
practical. Lastly, applications of OSR are highlighted and some new directions
for future research topics are suggested.
