BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced
  Feature-Level Contrastive Learning
Qianhan Feng Lujing Xie Shijie Fang Tong Lin
Semi-supervised Learning (SSL) reduces the need for extensive annotations in
deep learning, but the more realistic challenge of imbalanced data distribution
in SSL remains largely unexplored. In Class Imbalanced Semi-supervised Learning
(CISSL), the bias introduced by unreliable pseudo-labels can be exacerbated by
imbalanced data distributions. Most existing methods address this issue at
instance-level through reweighting or resampling, but the performance is
heavily limited by their reliance on biased backbone representation. Some other
methods do perform feature-level adjustments like feature blending but might
introduce unfavorable noise. In this paper, we discuss the bonus of a more
balanced feature distribution for the CISSL problem, and further propose a
Balanced Feature-Level Contrastive Learning method (BaCon). Our method directly
regularizes the distribution of instances' representations in a well-designed
contrastive manner. Specifically, class-wise feature centers are computed as
the positive anchors, while negative anchors are selected by a straightforward
yet effective mechanism. A distribution-related temperature adjustment is
leveraged to control the class-wise contrastive degrees dynamically. Our method
demonstrates its effectiveness through comprehensive experiments on the
CIFAR10-LT, CIFAR100-LT, STL10-LT, and SVHN-LT datasets across various
settings. For example, BaCon surpasses instance-level method FixMatch-based ABC
on CIFAR10-LT with a 1.21% accuracy improvement, and outperforms
state-of-the-art feature-level method CoSSL on CIFAR100-LT with a 0.63%
accuracy improvement. When encountering more extreme imbalance degree, BaCon
also shows better robustness than other methods.