  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
https://github.com/LeapLabTHU/SimPro.
