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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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HDFLStyler: Hierarchical domain-invariant feature learning for source-free domain generalization.

Deqian Mao1, Shanshan Gao2, Faqiang Huang1

  • 1School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan, 250014, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 16, 2026
PubMed
Summary

This study introduces HDFLStyler for source-free domain generalization, enhancing classification accuracy by generating diverse styles from text prompts and learning domain-invariant features. Experiments show excellent performance in generalizing models to new domains without source data.

Keywords:
ClassificationDiverse styleDomain-invariant featuresSource-free domain generalizationVision-language models

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Source-Free Domain Generalization (SFDG) aims to develop models adaptable to new domains without access to original training data.
  • Current SFDG methods often rely on vision-language models and text prompts for style feature extraction, facing challenges in generating diverse styles and learning domain-invariant features.

Purpose of the Study:

  • To propose a novel hierarchical domain-invariant feature learning method (HDFLStyler) to improve classification accuracy in SFDG.
  • To address the challenge of generating diverse styles solely from text prompts and learning robust domain-invariant features.

Main Methods:

  • Developed a diverse style generation module using random distribution adjustment and adaptive mixing strategies.
  • Implemented a domain-invariant feature learning component combining global and local feature extraction.
  • Introduced a domain-invariant consistency loss to enhance feature learning.

Main Results:

  • HDFLStyler demonstrated superior classification performance in SFDG tasks.
  • The method effectively generates diverse styles and learns domain-invariant features from text prompts.
  • Extensive experiments validated the efficacy of the proposed approach.

Conclusions:

  • HDFLStyler offers an effective solution for SFDG by improving style diversity and domain-invariant feature learning.
  • The proposed method enhances model generalization capabilities without requiring source domain data.
  • This work contributes to advancing SFDG techniques through innovative style generation and feature learning strategies.