research-article

Transfer Learning with Dynamic Distribution Adaptation

Authors:

Qiang YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 1

Article No.: 6, Pages 1 - 25

https://doi.org/10.1145/3360309

Published: 06 February 2020 Publication History

Abstract

Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this article, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and set up a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance, which leads to better performance. We believe this observation can be helpful for future research in transfer learning.

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Index Terms

Transfer Learning with Dynamic Distribution Adaptation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Learning under covariate shift
        Transfer learning
      2. Unsupervised learning
        Dimensionality reduction and manifold learning

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 1

February 2020

304 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3375625

Editor:
Yu Zheng
JD Finance, China

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Publication History

Published: 06 February 2020

Accepted: 01 September 2019

Revised: 01 July 2019

Received: 01 March 2019

Published in TIST Volume 11, Issue 1

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Beijing Municipal Science 8 Technology Commission
Hong Kong CERG projects
Nanyang Technological University
Nanyang Assistant Professorship (NAP)
National Key R 8 D Program of China

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