期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 10, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3293318
关键词
Zero-shot learning survey
资金
- National Research Foundation, Prime Minister's Office, Singapore under its IDM Futures Funding Initiative
- Interdisciplinary Graduate School, Nanyang Technological University, Singapore
- Nanyang Assistant Professorship (NAP)
- Nanyang Technological University - Ng Teng Fong Charitable Foundation
- Peking University - Ng Teng Fong Charitable Foundation
Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes we aim to classify are disjoint. In this paper, we provide a comprehensive survey of zero-shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we describe different semantic spaces adopted in existing zero-shot learning works. Third, we categorize existing zero-shot learning methods and introduce representative methods under each category. Fourth, we discuss different applications of zero-shot learning. Finally, we highlight promising future research directions of zero-shot learning.
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