用于无监督领域适应的动态参数化学习

IF 2.7 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers of Information Technology & Electronic Engineering Pub Date : 2023-12-07 DOI:10.1631/fitee.2200631
Runhua Jiang, Yahong Han
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引用次数: 0

摘要

无监督领域适应使神经网络能够通过学习领域不变表征,从有标记的源领域转移到无标记的目标领域。最近的方法通过直接匹配这两个域的边际分布来实现这一目标。然而,大多数方法都忽略了对领域匹配和语义辨别学习之间动态权衡的探索,因此容易出现负迁移和离群样本的问题。为了解决这些问题,我们引入了动态参数化学习框架。首先,通过探索领域级语义知识,提出了动态配准参数,以自适应地调整领域配准和语义判别学习的优化步骤。此外,为了获得语义判别和领域不变的表征,我们提出在源领域和目标领域上对齐训练轨迹。为了验证所提方法的有效性,我们进行了全面的实验,并在三个视觉任务的七个数据集上进行了广泛的比较,以证明这些方法的实用性。
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Dynamic parameterized learning for unsupervised domain adaptation

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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来源期刊
Frontiers of Information Technology & Electronic Engineering
Frontiers of Information Technology & Electronic Engineering COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.00
自引率
10.00%
发文量
1372
期刊介绍: Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.
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