@inproceedings{Fan_CVPR2022,
TITLE = {{CoSSL}: {C}o-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning},
AUTHOR = {Fan, Yue and Dai, Dengxin and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-6946-3},
DOI = {10.1109/CVPR52688.2022.01417},
PUBLISHER = {IEEE},
YEAR = {2022},
ABSTRACT = {In this paper, we propose a novel co-learning framework (CoSSL) with<br>decoupled representation learning and classifier learning for imbalanced SSL.<br>To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE)<br>for classifier learning. Furthermore, the current evaluation protocol for<br>imbalanced SSL focuses only on balanced test sets, which has limited<br>practicality in real-world scenarios. Therefore, we further conduct a<br>comprehensive evaluation under various shifted test distributions. In<br>experiments, we show that our approach outperforms other methods over a large<br>range of shifted distributions, achieving state-of-the-art performance on<br>benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our<br>code will be made publicly available.<br>},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
PAGES = {14554--14564},
ADDRESS = {New Orleans, LA, USA},
}