Generalizing from a Few Examples

DOI Web Site 被引用文献7件
  • Yaqing Wang
    Hong Kong University of Science and Technology and Baidu Research, Beijing, China
  • Quanming Yao
    4Paradigm Inc., Kwun Tong, Kowloon, Hong Kong
  • James T. Kwok
    >Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
  • Lionel M. Ni
    Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

書誌事項

タイトル別名
  • A Survey on Few-shot Learning

説明

<jats:p>Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this article, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience; (ii) model, which uses prior knowledge to reduce the size of the hypothesis space; and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications, and theories, are also proposed to provide insights for future research.<jats:sup>1</jats:sup></jats:p>

収録刊行物

  • ACM Computing Surveys

    ACM Computing Surveys 53 (3), 1-34, 2020-06-12

    Association for Computing Machinery (ACM)

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