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2019 | OriginalPaper | Chapter

Attention Relational Network for Few-Shot Learning

Authors : Jia Shuai, JiaMing Chen, Meng Yang

Published in: Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Publisher: Springer International Publishing

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Abstract

Few-shot learning aims to learn a model which can quickly generalize with only a small number of labeled samples per class. The situation we consider is how to use the information of the test set to generate the better prototype representation of the training set. In this paper, based on attention mechanism we propose a flexible and efficient framework for few-shot feature fusion, called Attention Relational Network (ARN) which is a three-branch structure of embedding module, weight module and matching module. Specifically, with attention mechanism, the proposed ARN can model adaptively the constribution weights of sample features from embedding module and then generate the prototype representations by weighted fusion of the sample features. Finally, the matching module identify target sample by calculating the matching scores. We evaluated this method on the MiniImageNet and Omniglot dataset, and the experiment proved that our method is very attractive.

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Metadata
Title
Attention Relational Network for Few-Shot Learning
Authors
Jia Shuai
JiaMing Chen
Meng Yang
Copyright Year
2019
Book
Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Print ISBN: 978-3-030-36203-4

Electronic ISBN: 978-3-030-36204-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-36204-1_13

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