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Updated: Sep 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Comprehensive disentanglement with fine-grained feature mitigation for domain generalization.

Youjia Shao1, Changshuo Wang2, Qihang Jia1

  • 1College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 28, 2025
PubMed
Summary

This study introduces a new method for domain generalization that disentangles features to improve model performance on unseen data. The approach enhances generalization by separating domain-invariant and domain-specific features for more stable learning.

Keywords:
Class discriminabilityDomain generalizationFeature disentanglementFeature mitigationLabel smoothing

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Domain generalization addresses the challenge of domain shift, where models trained on one data distribution fail on others.
  • Existing methods often struggle with unstable domain-specific features during domain-invariant representation learning.

Purpose of the Study:

  • To develop a novel method for comprehensive and explicit feature disentanglement in domain generalization.
  • To improve the generalization ability of models by reducing reliance on spurious domain-specific features and enhancing stable semantics.

Main Methods:

  • Proposed Source Split-flow Disentanglement with Smoothness-Fine-grained Feature Mitigation (SSDS-FFM) paradigm.
  • Implemented a source split-flow structure with independent domain-invariant and domain-specific feature extractors.
  • Utilized mutual information minimization, domain label smoothing, and selective reverse contrastive learning for feature disentanglement and enhancement.

Main Results:

  • Achieved comprehensive feature disentanglement, separating domain-invariant and domain-specific features effectively.
  • Demonstrated improved generalization ability through stable domain-invariant representation learning.
  • Validated effectiveness and superiority on benchmark datasets: PACS, VLCS, Office-Home, and DomainNet.

Conclusions:

  • The proposed SSDS-FFM paradigm offers a logical approach to achieve robust domain generalization.
  • Comprehensive feature disentanglement leads to enhanced model performance on unseen domains.
  • The method successfully mitigates local domain misalignment and feature space compression.