## 1 Introduction  
Early image classification research [1; 2] relied on the manual collection of image features, such as color and texture, to classify images accurately. However, this early work was time-consuming and laborious and may cause the misclassification of images due to the large number of pictures and people's inattentiveness. In recent years, with the rise of neural networks [3; 4; 5; 6; 7] and large datasets, tremendous progress has been made in many tasks in computer vision, such as image classification [8; 9], image segmentation [10; 11], and object detection [12; 13; 14]. This paper focuses on semi-supervised image classification methods, which learn sample features from a small portion of labeled data and a large amount of unlabeled data to classify images accurately. This ensures less human workload and higher accuracy of image classification. Based on an extensive review [15; 16; 17; 18], it can be found that the existing semi-supervised learning methods can be broadly classified into three categories. (1) Adversarial learning-based methods [19; 20; 21; 22], (2) Graph-based methods [23; 24], and (3) Consistency strategy-based methods [25; 26; 27; 28; 29; 30; 31; 32; 33]. The adversarial learning-based methods [19; 20; 21; 22] generate many artificial images to complement the actual training samples by learning the underlying distribution of real images. The aim is to obtain CNN models with better performance by increasing the training data. In recent years, generative adversarial networks and their variants [34; 35; 36; 37] have been extensively studied and applied to semi-supervised and unsupervised learning with good results. Methods based on consistency strategies [30; 31; 32; 33] effectively use information from unlabeled data by making the two predicted values of different images produced by random enhancement consistent. For example, pseudo-label [25] uses the network output directly as the consistency target. Temporal ensembling [26] uses the exponential moving average (EMA) predictions from each unlabeled data as the consistency target, mainly improving the quality of the target. The mean teacher [27] framework does not retain the exponential moving average (EMA) predictions. Instead, it uses the exponential average weights from the student model to reconstruct the teacher model, which ensures target quality and eliminates the redundant matrix information associated with exponential average shifts. VAT [28] enables the model to generate more reliable consistency targets by enhancing local smoothing of the label distribution for a given input. [PERSON] et al. [29] judged sample predictions and rejected and excluded unreliable samples. Focal loss [38] and reduced focal loss [39] focus more on hard-to-classify examples by simply weighting the losses. [PERSON] et al. [40; 41] have unexpected effects by exploring sample information within the network. In this paper, a new semi-supervised classification model is proposed. Specifically, in the supervised part, the image generation model generates several artificial samples with labels (with \"almost the same\" underlying distribution as the actual training samples), aiming to complement the limited number of labeled samples; secondly, the sample labels are mapped to the interior of the network as a \"benchmark\" to compare with the internal features of the samples as part of the supervised loss. Finally, the original supervisory loss is weighted to enable the network to correct misclassified samples accurately. In the unsupervised part, unlike the actual unsupervised loss, we filter the samples individually so that the network parameters are dominated by the features of the examples judged to be correct. The above process allows the semi-supervised model to learn sample features more accurately, thus enabling the model to classify images accurately. The main contributions are as follows:  
1. A new image generation model is proposed to generate artificial samples designed to complement the limited number of labeled samples in the supervised modules.