CLC number: TP391
On-line Access: 2023-12-04
Received: 2022-12-09
Revision Accepted: 2023-12-05
Crosschecked: 2023-04-13
Cited: 0
Clicked: 1353
Citations: Bibtex RefMan EndNote GB/T7714
Runhua JIANG, Yahong HAN. Dynamic parameterized learning for unsupervised domain adaptation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1616-1632.
@article{title="Dynamic parameterized learning for unsupervised domain adaptation",
author="Runhua JIANG, Yahong HAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="11",
pages="1616-1632",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200631"
}
%0 Journal Article
%T Dynamic parameterized learning for unsupervised domain adaptation
%A Runhua JIANG
%A Yahong HAN
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 11
%P 1616-1632
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200631
TY - JOUR
T1 - Dynamic parameterized learning for unsupervised domain adaptation
A1 - Runhua JIANG
A1 - Yahong HAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 11
SP - 1616
EP - 1632
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200631
Abstract: unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between domain alignment and semantic discrimination learning, thus rendering them susceptible to the problems of negative transfer and outlier samples. To address these issues, we introduce the dynamic parameterized learning framework. First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the optimization steps of domain alignment and semantic discrimination learning. Besides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align training trajectories on both source and target domains. Comprehensive experiments are conducted to validate the effectiveness of the proposed methods, and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.
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