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CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning

MPG-Autoren
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Fan,  Yue
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Dai,  Dengxin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Schiele,  Bernt       
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Zitation

Fan, Y., Dai, D., & Schiele, B. (2022). CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14554-14564). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.01417.


Zitierlink: https://hdl.handle.net/21.11116/0000-000A-16BA-C
Zusammenfassung
In this paper, we propose a novel co-learning framework (CoSSL) with
decoupled representation learning and classifier learning for imbalanced SSL.
To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE)
for classifier learning. Furthermore, the current evaluation protocol for
imbalanced SSL focuses only on balanced test sets, which has limited
practicality in real-world scenarios. Therefore, we further conduct a
comprehensive evaluation under various shifted test distributions. In
experiments, we show that our approach outperforms other methods over a large
range of shifted distributions, achieving state-of-the-art performance on
benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our
code will be made publicly available.