Semi-supervised learning (SSL) has shown great promise in leveraging
unlabeled data to improve model performance. While standard SSL assumes uniform
data distribution, we consider a more realistic and challenging setting called
imbalanced SSL, where imbalanced class distributions occur in both labeled and
unlabeled data. Although there are existing endeavors to tackle this challenge,
their performance degenerates when facing severe imbalance since they can not
reduce the class imbalance sufficiently and effectively. In this paper, we
study a simple yet overlooked baseline -- SimiS -- which tackles data imbalance
by simply supplementing labeled data with pseudo-labels, according to the
difference in class distribution from the most frequent class. Such a simple
baseline turns out to be highly effective in reducing class imbalance. It
outperforms existing methods by a significant margin, e.g., 12.8%, 13.6%, and
16.7% over previous SOTA on CIFAR100-LT, FOOD101-LT, and ImageNet127
respectively. The reduced imbalance results in faster convergence and better
pseudo-label accuracy of SimiS. The simplicity of our method also makes it
possible to be combined with other re-balancing techniques to improve the
performance further. Moreover, our method shows great robustness to a wide
range of data distributions, which holds enormous potential in practice. Code
will be publicly available.

SimiS 是一种简单而有效的 SSL 算法，通过用伪标签补充数据以解决类别不平衡问题，在 CIFAR100-LT，FOOD101-LT 和 ImageNet127 数据集上分别相对现有方法提高了 12.8％，13.6％和 16.7％的表现。