survey

A Survey of Zero-Shot Learning: Settings, Methods, and Applications

Authors:

Vincent W. Zheng,

Chunyan MiaoAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 10, Issue 2

Article No.: 13, Pages 1 - 37

https://doi.org/10.1145/3293318

Published: 16 January 2019 Publication History

Abstract

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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Index Terms

A Survey of Zero-Shot Learning: Settings, Methods, and Applications
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 2

Survey Papers and Regular Papers

March 2019

214 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3306498

Editor:
Yu Zheng
JD Finance, China

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Publication History

Published: 16 January 2019

Accepted: 01 September 2018

Revised: 01 September 2018

Received: 01 February 2018

Published in TIST Volume 10, Issue 2

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