Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation-Reference-Cited by-同舟云学术

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Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation

Published:2022-01 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Ahmed Waqar¹,Morerio Pietro¹,Murino Vittorio¹

Affiliation:

1. Istituto Italiano di Tecnologia,Pattern Analysis & Computer Vision,Genova,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9706406/9706408/09706833.pdf?arnumber=9706833

Reference47 articles.

1. Unbiased look at dataset bias

2. Discriminative Adversarial Domain Adaptation

3. VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation

4. Moment Matching for Multi-Source Domain Adaptation

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2. Adaptive Fuzzy Positive Learning for Annotation-Scarce Semantic Segmentation;International Journal of Computer Vision;2024-09-02

3. Unified multi-level neighbor clustering for Source-Free Unsupervised Domain Adaptation;Pattern Recognition;2024-09

4. A Comprehensive Survey on Source-Free Domain Adaptation;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-08

5. A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts;International Journal of Computer Vision;2024-07-18

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