We address the challenging problem of Long-Tailed Semi-Supervised Learning (LTSSL) where labeled data exhibit imbalanced class distribution and unlabeled data follow an unknown distribution. Unlike in balanced SSL, the generated pseudo-labels are skewed towards head classes, intensifying the training bias. Such a phenomenon is even amplified as more unlabeled data will be mislabeled as head classes when the class distribution of labeled and unlabeled datasets are mismatched. To solve this problem, we propose a novel method named ComPlementary Experts (CPE). Specifically, we train multiple experts to model various class distributions, each of them yielding high-quality pseudo-labels within one form of class distribution. Besides, we introduce Classwise Batch Normalization for CPE to avoid performance degradation caused by feature distribution mismatch between head and non-head classes. CPE achieves state-of-the-art performances on CIFAR-10-LT, CIFAR-100-LT, and STL-10-LT dataset benchmarks. For instance, on CIFAR-10-LT, CPE improves test accuracy by over >2.22% compared to baselines. Code is available at https://github.com/machengcheng2016/CPE-LTSSL.

我们提出了一种名为CPE的新方法，通过训练多个专家来解决长尾半监督学习中标签不平衡和未标记数据分布未知的问题，并引入CPE的类别批量归一化来避免特征分布不匹配造成的性能下降。在CIFAR-10-LT、CIFAR-100-LT和STL-10-LT数据集基准上，CPE取得了最先进的性能，例如在CIFAR-10-LT上，与基线相比，CPE将测试准确率提高了超过2.22％。

三个头胜过一个：长尾半监督学习的互补专家