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Updated: Sep 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Reweighting balanced representation learning for long tailed image recognition in multiple domains.

Panpan Fu1,2, Nur Intan Raihana Ruhaiyem3, Jiangtao Wang2

  • 1School of Informatics and Engineering, Suzhou University, Suzhou, 234000, China.

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|July 4, 2025
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Summary

This study introduces a balanced representation learning (BRL) algorithm to tackle data imbalance in multi-domain learning for image recognition. BRL effectively reduces biases, improving classifier performance, particularly for challenging classes.

Keywords:
Long-tailedMulti-domainRe-weightingRepresentation learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-domain long-tailed learning presents challenges due to within-domain class imbalance and across-domain sample proportion variation.
  • These imbalances introduce significant biases in covariate and representation learning, hindering the extraction of domain-invariant features.

Purpose of the Study:

  • To apply and evaluate an advanced reweighting balanced representation learning (BRL) algorithm for multi-domain long-tailed image recognition.
  • To address biases in both input and latent spaces caused by data imbalance.

Main Methods:

  • The study integrates covariate and representation balancing techniques within a reweighting-based class balancing framework.
  • The balanced representation learning (BRL) algorithm is applied to multi-domain long-tailed image recognition tasks.

Main Results:

  • Extensive evaluations on six benchmark datasets demonstrate BRL's effectiveness.
  • The algorithm successfully extracts domain- and class-unbiased feature representations.
  • Significant improvements in classifier performance were observed, especially for the most imbalanced classes.

Conclusions:

  • The balanced representation learning (BRL) algorithm offers a robust solution for multi-domain long-tailed image recognition.
  • This approach shows promise for applications in fields like environmental monitoring and medical imaging, addressing critical data imbalance issues.