Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Label Space-Induced Pseudo Label Refinement (LPR) for multi-source black-box domain adaptation (MSBDA). LPR refines pseudo-labels using source API predictions, enhancing target model adaptation without source data access.
Area Of Science
- Machine Learning
- Artificial Intelligence
- Computer Vision
Background
- Unsupervised Domain Adaptation (UDA) typically requires source data/models, posing privacy and IP concerns.
- Black-box Domain Adaptation (BDA) uses API predictions for pseudo-labeling, but struggles with multi-source settings.
- Existing multi-source BDA methods lack effective pseudo-label generation strategies.
Purpose Of The Study
- To develop a novel training framework for multi-source black-box domain adaptation (MSBDA).
- To introduce a method that refines pseudo-labels by learning relationships among multiple source domains.
- To enable effective target model adaptation using only source API predictions.
Main Methods
- Proposed Label Space-Induced Pseudo Label Refinement (LPR) framework for MSBDA.
- Introduced a Pseudo label Refinery Network (PRN) to learn inter-source domain relationships.
- Employed a dual-phase PRN: a warm-up for initial pseudo-labels and a refinement phase for improved accuracy.
Main Results
- LPR effectively refines pseudo-labels by leveraging relationships between source domains.
- The dual-phase PRN successfully adapts the target model, mitigating noisy samples.
- Achieved competitive performance on four benchmark datasets in various domain adaptation settings.
Conclusions
- LPR offers a robust solution for MSBDA, overcoming limitations of existing BDA methods.
- The framework demonstrates the efficacy of pseudo-label refinement through learned domain relationships.
- Provides theoretical support for the proposed mechanism, validating its effectiveness.
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