FGBC: Flexible graph-based balanced classifier for class-imbalanced semi-supervised learning

Xiangyuan Kong, Xiang Wei, Xiaoyu Liu, Jingjie Wang, Weiwei Xing, Wei Lu

Abstract: Highlights•We propose a graph-based auxiliary balanced classifier head that is attached to the existing SSL to construct an end-2-end learning framework for efficient label propagation in CISSL.•We propose a simple yet effective flexible threshold adjustment strategy of leveraging different learning status of different classes for proper pseudo-label sieving.•We propose a class-aware feature MixUp (CFM) augmentation algorithm to adaptively augment training features for mitigating the over-fitting problem of tail classes.•Our FGBC framework shows favorable performance on the prevalent CISSL image classification datasets CIFAR10/100-LT, SVHN-LT, and Small ImageNet-127 with various levels of imbalance ratios and labeled ratios. The code will be available.