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CLC number: TP391

On-line Access: 2023-12-04

Received: 2022-12-09

Revision Accepted: 2023-12-05

Crosschecked: 2023-04-13

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yahong Han

https://orcid.org/0000-0003-2768-1398

Runhua JIANG

https://orcid.org/0000-0003-2402-8684

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.11 P.1616-1632

http://doi.org/10.1631/FITEE.2200631


Dynamic parameterized learning for unsupervised domain adaptation


Author(s):  Runhua JIANG, Yahong HAN

Affiliation(s):  College of Intelligence and Computing, Tianjin University, Tianjin 300350, China; more

Corresponding email(s):   ddghjikle1@gmail.com, yahong@tju.edu.cn

Key Words:  Unsupervised domain adaptation, Optimization steps, Domain alignment, Semantic discrimination


Runhua JIANG, Yahong HAN. Dynamic parameterized learning for unsupervised domain adaptation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1616-1632.

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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.

无监督域自适应的动态参数化学习

蒋润华1,2,韩亚洪1,2
1天津大学智能与计算学部,中国天津市,300350
2天津大学天津市机器学习重点实验室,中国天津市,300350
摘要:无监督领域自适应通过学习域不变表示实现神经网络从有标签数据组成的源域到无标签数据组成的目标域迁移。近期研究通过直接匹配这两个域的边缘分布实现这一目标。然而,已有研究大多数忽略域对齐和语义判别学习之间的动态平衡,因此容易受负迁移和异常样本影响。为解决这些问题,引入动态参数化学习框架。首先,通过探索领域级语义知识,提出动态对齐参数自适应地调整域对齐和语义判别学习的优化过程。此外,为获得判别能力强和域不变的表示,提出在源域和目标域上对齐优化过程。本文通过综合实验证明了所提出方法的有效性,并在3个视觉任务的7个数据集上进行广泛比较,证明可行性。

关键词:无监督领域自适应;优化步骤;跨域判别表示;语义判别

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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