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Dynamic parameterized learning for unsupervised domain adaptation

无监督域自适应的动态参数化学习

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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.

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.

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Data availability

The data that support the findings of this study are openly available in Transfer-Learning-Library at https://github.com/thuml/Transfer-Learning-Library.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Runhua Jiang  (蒋润华) & Yahong Han  (韩亚洪)

  2. Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, 300350, China

    Runhua Jiang  (蒋润华) & Yahong Han  (韩亚洪)

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  1. Runhua Jiang  (蒋润华)
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  2. Yahong Han  (韩亚洪)
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Contributions

Runhua JIANG designed the research and drafted the paper. Yahong HAN helped organize the paper. Runhua JIANG revised and finalized the paper.

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Correspondence to Yahong Han  (韩亚洪).

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Yahong HAN is a corresponding expert of Frontiers of Information Technology & Electronic Engineering, and he was not involved with the peer review process of this paper. Runhua JIANG and Yahong HAN declare that they have no conflict of interest.

Additional information

Project supported by the National Natural Science Foundation of China (No. 61932009) and the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study, China

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Jiang, R., Han, Y. Dynamic parameterized learning for unsupervised domain adaptation. Front Inform Technol Electron Eng 24, 1616–1632 (2023). https://doi.org/10.1631/FITEE.2200631

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