# ReliaMatch: Semi-Supervised Classification with Reliable Match
## 1 Introduction  
In the past decade, deep learning has dominated the machine learning landscape in data classification [1; 2; 3], predictive analysis [4], recommendation systems [5], anomaly detection [6; 7] and so on. Within deep learning, supervised classification methods have significantly improved the performance of deep learning in various classification tasks. However, it is still very difficult to obtain labels provided by professionals in many big data application scenarios. In contrast, unsupervised classification methods have obvious advantages in dealing with unlabeled samples. Nevertheless, they greatly sacrificed the accuracy of the model because they cannot directly evaluate and optimize the performance of deep learning models by using the label information. Therefore, semi-supervised learning [8] caught the attention of researchers, and significantly improved the model performance of unsupervised learning by leveraging datasets with a small amount of label data. A more practical value lies in semi-supervised learning methods being able to reduce the cost and time of manually marking data. Recently, many semi-supervised learning methods based on deep learning have been proposed [9; 10; 11], which can achieve quite a good performance by leveraging the small fraction of labeled samples in the dataset. However, semi-supervised learning faces two major challenges: (_i_) how to transfer information obtained from limited labeled data to unlabeled data, and (_ii_) how to learn as accurate information as possible directly from a large amount of unlabeled data. To addressthese issues, semi-supervised learning uses three key loss terms (i.e., entropy minimization, generalization regularization, and consistency regularization) to better motivate the model to learn the corresponding downstream tasks. Among them, entropy minimization can encourage the model to confidently predict the output of unlabeled data, thereby improving the accuracy and robustness of the model. Generalization regularization constrains the model's parameters to avoid overfitting to the training data during the training process, thereby improving the model's generalization performance. According to the consistency assumption, data points that are close to each other often have consistency in the same label and structure [12]. Therefore, consistency regularization can improve the accuracy and generalization ability of the model by making the data consistent on the manifold. In addition, many studies have explored methods for solving the information propagation problem between different data [13], providing ideas for addressing the first major issue in semi-supervised learning. Among them, pseudo-labeling and consistency regularization methods provide ideas for solving the second major issue in semi-supervised learning. The pseudo-labeling method [14] uses the predictions of a classification model or a clustering algorithm as artificial labels to retrain the model. The consistency regularization method [10; 11; 15] forces the model to make the same prediction for the same sample under different transformations, learning from unlabeled data. However, these semi-supervised learning methods do not consider the possibility of introducing different levels of erroneous information during the training process, which can lead to low classification accuracy. In the feature extraction stage, as plotted in Figure 1 (left), the model may have difficulty accurately differentiating between semantic differences at the classification boundary due to the ambiguity of feature representation boundaries. Without setting anchors for each class, the model may learn incorrect semantic information. However, by providing each class with an anchor, confidence thresholds can be set using the similarity between each sample and the anchor, allowing low-confidence features to be filtered out as shown in Figure 1 (right).