Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabelled data to alleviate models' dependence on large labelled datasets. The common framework among recent approaches is to train the model on a large amount of unlabelled data with consistency regularization to constrain the model predictions to be invariant to input perturbation. However, the existing SSL frameworks still have room for improvement in the consistency regularization method. Instead of regularizing category predictions in the label space as in existing frameworks, this paper proposes a feature space renormalization (FSR) mechanism for SSL. First, we propose a feature space renormalization mechanism to substitute for the commonly used consistency regularization mechanism to learn better discriminative features. To apply this mechanism, we start by building a basic model and an empirical model and then introduce our mechanism to renormalize the feature learning of the basic model with the guidance of the empirical model. Second, we combine the proposed mechanism with pseudo-labelling to obtain a novel effective SSL model named FreMatch. The experimental results show that our method can achieve better performance on a variety of standard SSL benchmark datasets, and the proposed feature space renormalization mechanism can also enhance the performance of other SSL approaches.

翻译：半监督学习已被证明是利用未标注数据减轻模型对大量标注数据依赖的有效方法。近期研究中的通用框架是通过在大量未标注数据上采用一致性正则化训练模型，使模型预测对输入扰动具有不变性。然而现有半监督学习框架在一致性正则化方法上仍有改进空间。本文提出了一种面向半监督学习的特征空间重整化机制（FSR），区别于现有框架中在标签空间进行类别预测正则化的做法。首先，我们提出特征空间重整化机制替代常用的一致性正则化机制，以学习更优的判别性特征。为应用该机制，我们首先构建基础模型和经验模型，随后引入所提机制，通过经验模型的指导对基础模型的特征学习进行重整化处理。其次，我们将该机制与伪标签技术相结合，构建了名为FreMatch的新型高效半监督学习模型。实验结果表明，我们的方法在多种标准半监督学习基准数据集上均能取得更优性能，且所提出的特征空间重整化机制亦能增强其他半监督学习方法的性能。