Computer Vision

Living Edition

Few-Shot Learning

  • Hugo LarochelleEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-030-03243-2_861-1
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Synonyms

Related Concepts

Definition

Few-shot learning refers to the machine learning problem of learning a model from very few examples (shots).

Background

Computer vision systems based on machine learning often require the collection of large datasets for their training. This is often a challenging obstacle for their deployment. Moreover, there is evidence that humans are actually able to learn concepts from very few examples [1, 2, 3]. Few-shot learning methods aim to reduce this observed gap between human learning and machine learning. This is achieved by performing a form of transfer learning using data from many previously observed tasks toward new tasks with little data.

Theory and Application

In the 2000s, early research in computer vision on few-shot learning tackled the problem by using hand-designed feature representations and focusing on the exploration of learning and inference algorithms operating on...

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Google Research, Brain Team & MilaMontrealCanada

Section editors and affiliations

  • Victor Lempitsky
    • 1
  1. 1.Samsung AI Center and Skolkovo Institute of Science and TechnologyMoscowRussia