Recent advancements in semi-supervised learning have focused on a more
realistic yet challenging task: addressing imbalances in labeled data while the
class distribution of unlabeled data remains both unknown and potentially
mismatched. Current approaches in this sphere often presuppose rigid
assumptions regarding the class distribution of unlabeled data, thereby
limiting the adaptability of models to only certain distribution ranges. In
this study, we propose a novel approach, introducing a highly adaptable
framework, designated as SimPro, which does not rely on any predefined
assumptions about the distribution of unlabeled data. Our framework, grounded
in a probabilistic model, innovatively refines the expectation-maximization
(EM) algorithm by explicitly decoupling the modeling of conditional and
marginal class distributions. This separation facilitates a closed-form
solution for class distribution estimation during the maximization phase,
leading to the formulation of a Bayes classifier. The Bayes classifier, in
turn, enhances the quality of pseudo-labels in the expectation phase.
Remarkably, the SimPro framework not only comes with theoretical guarantees but
also is straightforward to implement. Moreover, we introduce two novel class
distributions broadening the scope of the evaluation. Our method showcases
consistent state-of-the-art performance across diverse benchmarks and data
distribution scenarios. Our code is available at
this https URL

在这项研究中，我们提出了一个新颖的、高度适应的框架 SimPro，它不依赖于对未标记数据分布的任何预定义假设。基于一种概率模型，我们创新地改进了期望最大化（EM）算法，通过明确分离条件和边际类别分布的建模来实现。这种分离在最大化阶段为类别分布估计提供了一个闭合形式的解，从而推导出贝叶斯分类器。而贝叶斯分类器则提高了期望阶段中伪标签的质量。值得注意的是，SimPro 框架不仅具备理论保证，而且易于实现。我们还引入了两种新颖的类别分布，扩大了评估的范围。我们的方法在不同基准和数据分布场景下展示了一致的最先进性能。