# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 2 Related Works
### Semi-Supervised Learning  
Semi-supervised learning (SSL) utilizes unlabeled data to improve feature representation given limited labeled data. Two main branches of methods have been proposed in recent years, namely, consistency regularization and pseudo-labeling. Consistency regularization is based on the cluster assumption, which states that the predicted result should not change significantly with minor perturbations because data points with different labels are separated in low-density regions. Given an unlabeled data point \(x\) and its perturbation \(\hat{x}\), SSL aims to minimize the distance between the two outputs. Popular distance measures are mean square error (MSE), Kullback-Leibler divergence (KL), and Jensen-Shannon divergence (JS). Consistency regularization enforces the current optimized model to yield stable and consistent predictions under various perturbations on the same unlabeled data. VAT [21] applies adversarial perturbations to the output. The \(\Pi\) model [2] applies different data augmentations and dropouts to form perturbed samples and aligns them. Dual struent [3] uses two networks with different initializations to generate perturbed outputs for the same input and aligns the different network outputs. MixMatch [22] and ReMixMatch [23] use data interpolation to generate perturbations. Pseudo-labeling can be roughly divided in two ways: self-training and multi-view training. Self-training [24] is a packaging algorithm repeatedly using supervised methods in the training process of each round. It uses the labeled data to train the model in the first round. The trained model predicts pseudo-labels for the unlabeled samples, and the high-confidence unlabeled samples are added to the training set for the re-training. There is no limit to the total training round. The advantage of self-training is that it is simple, but its learning performance depends on the supervised method used internally, and the modelcan hardly correct its errors, which may lead to error accumulation. The performance of this method is closely related to error labeling correction strategies and high-confidence unlabeled sample screening strategies. Multi-view training has more than one model in the training process; the unlabeled data and their pseudo-label generated by one model are used for another model in the next training round. Co-training [25] has two models \(m_{1}\) and \(m_{2}\) trained on different datasets. In each iteration, if either model considers its prediction result of sample X trustworthy, with confidence higher than the threshold \(\tau\), then that model will generate a pseudo-label for sample X and add the training sample X to the training set of the other model. Tri-training [26] is similar to Co-training, except that, in Tri-training, the newly acquired unlabeled training instances of one model are jointly determined by two other models. Tri-training first bootstraps the labeled sample set for repeatable sampling to obtain three labeled training sets and then trains a classifier from each labeled training set. In the cooperative training process, the new unlabeled samples with pseudo-labels obtained by each model are jointly provided by the other two classifiers. Specifically, suppose two classifiers predict the same unlabeled sample and have the same output. In that case, the sample is considered to have higher confidence and is added to the labeled training set of the third classifier after generating the pseudo-label. In addition to classification tasks, semi-supervised learning is widely used in semantic segmentation. In the semi-supervised semantic segmentation, CCT [27] enforces similar predictions under multiple perturbed embeddings. [PERSON] proposed a method [28] that enforces similar predictions under two different contextual crops. CPS [29] enforces consistency between dual differently initialized models. Similar to FixMatch [30], PseudoSeg [31] adapts the weak-to-strong consistency to the segmentation scenario and further applies a calibration module to refine the pseudo-masks.