计算机科学
分类器(UML)
人工智能
机器学习
训练集
开放式研究
模式识别(心理学)
领域(数学)
开放集
集合(抽象数据类型)
数学
离散数学
万维网
程序设计语言
纯数学
作者
Chuanxing Geng,Sheng-Jun Huang,Songcan Chen
标识
DOI:10.1109/tpami.2020.2981604
摘要
In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with the unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also overview the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.
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