Recent Advances in Open Set Recognition: A Survey
نویسندگان
چکیده
In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples exhaust all classes when a recognizer or classifier. A more realistic scenario open set recognition (OSR), where incomplete knowledge of the world exists at time, and unknown can be submitted an algorithm during testing, requiring classifiers not only accurately classify seen classes, but also effectively deal with unseen ones. This paper provides comprehensive survey existing techniques covering aspects ranging from related definitions, representations models, datasets, evaluation criteria, comparisons. Furthermore, we briefly analyze relationships between OSR its tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification reject option, so forth. Additionally, overview which as natural extension OSR. Importantly, highlight limitations approaches point out some promising subsequent research directions in this field.
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2021
ISSN: ['1939-3539', '2160-9292', '0162-8828']
DOI: https://doi.org/10.1109/tpami.2020.2981604