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Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation

Published: 04 March 2022 Publication History

Abstract

Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. However, these algorithms will be infeasible when only a few labeled data exist in the source domain, thus the performance decreases significantly. To address this challenge, we propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples. Firstly, DGL introduces the Nyström method to construct a plastic graph that shares similar geometric property with the target domain. Then, DGL flexibly employs the Nyström approximation error to measure the divergence between the plastic graph and source graph to formalize the distribution mismatch from the geometric perspective. Through minimizing the approximation error, DGL learns a domain-invariant geometric graph to bridge the source and target domains. Finally, we integrate the learned domain-invariant graph with the semi-supervised learning and further propose an adaptive semi-supervised model to handle the cross-domain problems. The results of extensive experiments on popular datasets verify the superiority of DGL, especially when only a few labeled source samples are available.

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Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 3
August 2022
478 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3505208
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2022
Accepted: 01 September 2021
Revised: 01 August 2021
Received: 01 December 2020
Published in TOMM Volume 18, Issue 3

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Author Tags

  1. Domain adaptation
  2. domain-invariant graph
  3. the Nyström method
  4. few labeled source samples

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  • Research-article
  • Refereed

Funding Sources

  • National Natural Science Foundation of China
  • Major Scientific and Technological Projects of CNPC
  • Open Project Program of the National Laboratory of Pattern Recognition (NLPR)

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