App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Meta-learning based on domain enhancement and feature alignment for single domain generalization

Sun Can
Hu Zhigang
Zheng Hao
School of Computer Science & Engineering, Central South University, Changsha 410083, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0585

Abstract

The single domain generalization(SDG) based on meta-learning has emerged as an effective technique for solving the domain-shift problem. However, the inconsistent semantic information between source and augmented domains and difficult separation of domain-invariant features from domain-related features make SDG model hard to achieve great generalization. To address the above problems, this paper proposed a novel meta-learning method based on domain enhancement and feature alignment (MetaDefa) to improve the model generalization performance. This method utilizes background replacement and visual damage techniques to generate diverse and effective augmented images for each image, ensuring the consistency of semantic information between the source domain and the enhanced domains. The multi-channel feature alignment module fully mines image information by focusing on similar target regions between the source and enhanced domains feature spaces and suppressing feature representations of non-target areas, thereby effectively extracting sufficient transferable knowledge. Through experimental evaluation, MetaDefa achieved 88.87%, 73.06% and 57.06% accuracy on office-Caltech-10, office31 and PACS datasets, respectively. The results show that the MetaDefa method successfully achieves semantic consistency between the source and augmented images and adequate extraction of domain-invariant features, which significantly improves the generalization performance of single domain generalization models.

Foundation Support

国家自然科学基金资助项目(62172442)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0585
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-04-08] Accepted Paper

Cite This Article

孙灿, 胡志刚, 郑浩. 单源域泛化中一种基于域增强和特征对齐的元学习方案 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0585. (Sun Can, Hu Zhigang, Zheng Hao. Meta-learning based on domain enhancement and feature alignment for single domain generalization [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0585. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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