无监督域自适应的动态参数化学习

蒋润华; 韩亚洪

doi:10.1631/FITEE.2200631

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无监督域自适应的动态参数化学习

常规文章 | Updated：2023-05-11

- 无监督域自适应的动态参数化学习
- Dynamic parameterized learning for unsupervised domain adaptation
- 信息与电子工程前沿（英文） 2023年24卷第11期页码：1616-1632
- Affiliations：
  
  1.College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
  2.Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin 300350, China
- Author bio：
  
  E-mail: ddghjikle1@gmail.com
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(61932009);Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study, China
- DOI：10.1631/FITEE.2200631
  中图分类号： TP391
- 纸质出版日期：2023-11-0 ，
  
  收稿日期：2022-12-09，
  
  录用日期：2023-04-13
- Accepted：
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蒋润华, 韩亚洪. 无监督域自适应的动态参数化学习[J]. 信息与电子工程前沿（英文）, 2023,24(11):1616-1632.

RUNHUA JIANG, YAHONG HAN. Dynamic parameterized learning for unsupervised domain adaptation. [J]. Frontiers of information technology & electronic engineering, 2023, 24(11): 1616-1632.
蒋润华, 韩亚洪. 无监督域自适应的动态参数化学习[J]. 信息与电子工程前沿（英文）, 2023,24(11):1616-1632. DOI： 10.1631/FITEE.2200631.

RUNHUA JIANG, YAHONG HAN. Dynamic parameterized learning for unsupervised domain adaptation. [J]. Frontiers of information technology & electronic engineering, 2023, 24(11): 1616-1632. DOI： 10.1631/FITEE.2200631.

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。

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.

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

Keywords

Unsupervised domain adaptationOptimization stepsDomain alignmentSemantic discrimination

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