Abstract: In recent years, semi-supervised learning (SSL) methods based on image augmentation and consistency regularization have been widely used and have achieved great success. However, little attention has been paid to pseudo-labeling (PL)-based semi-supervised learning methods because of the "confirmation bias" problem, i.e., errors in the model are accumulated by wrong pseudo-labels and thus difficult to be corrected. In this paper, we propose a feature refinement model based on the feature space graph. The model learns a graph attention model on the feature space mapped by the neural network. We apply this model to the feature space to make use of the information of the prototypes to refine the features. The pseudo-labels generated by the refined features are randomly and linearly combined with the pseudo-labels generated by the prototypes assignment to obtain new pseudo-labels. In this paper, we apply this module to two pseudo-labeling semi-supervised learning frameworks and achieve significant accuracy improvements in several CIFAR-10 and CIFAR-100 semi-supervised classification problems. In particular, we apply our feature refinement model to the pseudo-labeling semi-supervised learning framework PLCB and add the proposed mutual mix supervision techniques to achieve good results on this framework. By applying the proposed feature refinement module to several pseudo-labeling semi-supervised learning frameworks and conducting experiments on several datasets, the proposed algorithm is demonstrated to be universal and effective as an add-on module.

Key words: semi-supervised learning, pseudo-labeling, MixUp, graph attention model, prototype learning