  Open-set image recognition (OSR) aims to both classify known-class samples
and identify unknown-class samples in the testing set, which supports robust
classifiers in many realistic applications, such as autonomous driving, medical
diagnosis, security monitoring, etc. In recent years, open-set recognition
methods have achieved more and more attention, since it is usually difficult to
obtain holistic information about the open world for model training. In this
paper, we aim to summarize the up-to-date development of recent OSR methods,
considering their rapid development in recent two or three years. Specifically,
we firstly introduce a new taxonomy, under which we comprehensively review the
existing DNN-based OSR methods. Then, we compare the performances of some
typical and state-of-the-art OSR methods on both coarse-grained datasets and
fine-grained datasets under both standard-dataset setting and cross-dataset
setting, and further give the analysis of the comparison. Finally, we discuss
some open issues and possible future directions in this community.
