Addressing the class imbalance in long-tailed semi-supervised learning (SSL)
poses a few significant challenges stemming from differences between the
marginal distributions of unlabeled data and the labeled data, as the former is
often unknown and potentially distinct from the latter. The first challenge is
to avoid biasing the pseudo-labels towards an incorrect distribution, such as
that of the labeled data or a balanced distribution, during training. However,
we still wish to ensure a balanced unlabeled distribution during inference,
which is the second challenge. To address both of these challenges, we propose
a three-faceted solution: a flexible distribution alignment that progressively
aligns the classifier from a dynamically estimated unlabeled prior towards a
balanced distribution, a soft consistency regularization that exploits
underconfident pseudo-labels discarded by threshold-based methods, and a schema
for expanding the unlabeled set with input data from the labeled partition.
This last facet comes in as a response to the commonly-overlooked fact that
disjoint partitions of labeled and unlabeled data prevent the benefits of
strong data augmentation on the labeled set. Our overall framework requires no
additional training cycles, so it will align, distill, and augment everything
all at once (ADALLO). Our extensive evaluations of ADALLO on imbalanced SSL
benchmark datasets, including CIFAR10-LT, CIFAR100-LT, and STL10-LT with
varying degrees of class imbalance, amount of labeled data, and distribution
mismatch, demonstrate significant improvements in the performance of imbalanced
SSL under large distribution mismatch, as well as competitiveness with
state-of-the-art methods when the labeled and unlabeled data follow the same
marginal distribution. Our code will be released upon paper acceptance.

这篇研究提出了一种名为 ADALLO 的三方案框架，可解决长尾半监督学习中的类别不平衡问题，其中包括一个灵活的分布对齐机制、一个软一致性正则化机制和一个扩展未标记集的架构。该框架在不同程度的类别不平衡，不同数量的标记数据和分布不匹配的情况下在几个基准数据集上进行了评估，并展示了在大分布不匹配的情况下提高不平衡半监督学习表现的显著成果。