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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation.

Xuejun Zhao1, Rafal Stanislawski2, Paolo Gardoni3

  • 1CRRC Academy Co., Ltd., Beijing 100070, China.

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|June 10, 2022
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Summary

This study introduces label consistent contrastive learning (LCCL) for source-free unsupervised domain adaptation. LCCL effectively adapts models to new domains without source data, enhancing feature discrimination using a memory bank.

Keywords:
contrastive learningsource free domain adaptationunsupervised domain adaptation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Unsupervised domain adaptation (UDA) addresses domain shift but often requires source data.
  • Practical constraints like privacy and intellectual property limit source data availability.
  • Source-free UDA presents a more realistic challenge, adapting models without original training data.

Purpose of the Study:

  • To develop a novel framework for source-free unsupervised domain adaptation.
  • To enhance the discriminative power of features in the target domain.
  • To propose a generalizable method applicable to various UDA tasks.

Main Methods:

  • Introduced Label Consistent Contrastive Learning (LCCL), an adaptive contrastive learning framework.
  • Utilized a memory bank to store pseudo-labeled and clustered target domain samples.
  • Incorporated trusted historical samples into the contrastive learning process.

Main Results:

  • LCCL effectively encourages target domain samples to learn class-level discriminative features.
  • The proposed memory bank strategy facilitates contrastive learning without source data.
  • Demonstrated the generalizability of LCCL across different UDA scenarios.

Conclusions:

  • LCCL is a robust and effective approach for source-free unsupervised domain adaptation.
  • The method shows significant improvements on benchmark datasets for digit recognition and image classification.
  • LCCL offers a practical solution for domain adaptation challenges in privacy-sensitive applications.