research-article

Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation

Authors:

Changsheng XuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 3

Article No.: 72, Pages 1 - 18

https://doi.org/10.1145/3487194

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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Index Terms

Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation

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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

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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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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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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https://dl.acm.org/doi/10.1145/3659953
Han TZhou QYu JYu ZZhang JZhao S(2024)Effective Video Summarization by Extracting Parameter-Free Motion AttentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365467020:7(1-20)Online publication date: 16-May-2024
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