research-article

Few-Shot Text and Image Classification via Analogical Transfer Learning

Authors:

Alexander G. HauptmannAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 6

Article No.: 71, Pages 1 - 20

https://doi.org/10.1145/3230709

Published: 29 October 2018 Publication History

Abstract

Learning from very few samples is a challenge for machine learning tasks, such as text and image classification. Performance of such task can be enhanced via transfer of helpful knowledge from related domains, which is referred to as transfer learning. In previous transfer learning works, instance transfer learning algorithms mostly focus on selecting the source domain instances similar to the target domain instances for transfer. However, the selected instances usually do not directly contribute to the learning performance in the target domain. Hypothesis transfer learning algorithms focus on the model/parameter level transfer. They treat the source hypotheses as well-trained and transfer their knowledge in terms of parameters to learn the target hypothesis. Such algorithms directly optimize the target hypothesis by the observable performance improvements. However, they fail to consider the problem that instances that contribute to the source hypotheses may be harmful for the target hypothesis, as instance transfer learning analyzed. To relieve the aforementioned problems, we propose a novel transfer learning algorithm, which follows an analogical strategy. Particularly, the proposed algorithm first learns a revised source hypothesis with only instances contributing to the target hypothesis. Then, the proposed algorithm transfers both the revised source hypothesis and the target hypothesis (only trained with a few samples) to learn an analogical hypothesis. We denote our algorithm as Analogical Transfer Learning. Extensive experiments on one synthetic dataset and three real-world benchmark datasets demonstrate the superior performance of the proposed algorithm.

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Index Terms

Few-Shot Text and Image Classification via Analogical Transfer Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 6

Regular Papers

November 2018

290 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3289398

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2018 ACM.

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Publication History

Published: 29 October 2018

Accepted: 01 May 2018

Revised: 01 May 2018

Received: 01 January 2018

Published in TIST Volume 9, Issue 6

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Zhang KLuan XDing FLiu F(2024)A Bayesian transfer sparse identification method for nonlinear ARX systemsInternational Journal of Adaptive Control and Signal Processing10.1002/acs.3884Online publication date: 8-Aug-2024
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