# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 3 Results
### Performance Comparison  
1. CIFAR-10: For CIFAR-10, performance comparison experiments were conducted with six baselines, including MixMatch[33], Mean-Teacher[32], ICT[45], VAT[46], Temporal-ensembling[30], and Pimodel[30]. The accuracy of these methods was evaluated with a varying number of labeled samples from 250 to 4000 (as is standard practice). The result can be seen in Figure 6. It can be observed that MAC-SSL was significantly superior to all other methods, especially when labeled samples were scarce, such as with 250 labels and 500 labels. MAC-SSL outperformed the second-best method, MixMatch, by 6.82% and 7.02%, respectively. These results highlight MAC-SSL's ability to effectively utilize information from unlabeled data, thereby delivering a strong performance even in a scenario with limited labeled samples. 2. CIFAR-100: To further demonstrate the effectiveness of MAC-SSL, we conducted comparative experiments on CIFAR-100. The baselines for comparison were the same as those used for CIFAR-10. The CIFAR-100 evaluation involved a varying number of labeled samples ranging from 400 to 2500. The results are presented in Figure 7. Upon observation, it can be seen that MAC-SSL achieved the best performance. When the number of labeled samples was 400, which means only 4 labeled samples per class, MAC-SSL outperformed the second-best method, MixMatch, by 16.58%. It is also noteworthy that as the number of labeled samples decreased, MAC-SSL brought even greater improvements, further validating the ability of the proposed method to effectively utilize information from unlabeled samples. Figure 6: Accuracy (%) of CIFAR-10. Exact numbers are provided in Table 1 (Appendix A). Figure 7: Accuracy (%) of CIFAR-100. Exact numbers are provided in Table 1 (Appendix A). 3. SVHN: We conducted comparative experiments on the SVHN dataset and, in addition to CIFAR-10 and CIFAR-100, included CoMatch as one of the baselines. The accuracy of these methods was evaluated with varying numbers of labeled samples ranging from 250 to 4000. The experimental results are presented in Figure 8. As observed earlier, MAC-SSL achieved the best results and demonstrated greater improvements when the number of labeled samples was reduced. This once again confirmed the effectiveness of MAC-SSL and its ability to fully utilize unlabeled samples. Through the conducted experiments, the proposed method demonstrated superior performance compared to all other existing methods, particularly in scenarios with limited availability of labeled samples. The key advantage of our approach was its ability to effectively leverage the valuable information contained within unlabeled samples, which is often overlooked by other methods' data augmentation techniques. MAC-SSL stood out by generating reliable positive sample pairs through three distinct augmentation techniques, thereby enhancing the regularization benefits. Furthermore, by incorporating MixUP with augmented images and their corresponding labels or pseudo-labels, our model exhibited enhanced generalization capabilities and achieved efficient convergence. Notably, MAC-SSL exhibited exceptional classification performances across multiple benchmark datasets, including CIFAR-10, CIFAR-100, and SVHN. These results highlight the versatility and robustness of our approach, especially in scenarios where labeled samples are scarce. The promising outcomes achieved by MAC-SSL validate its potential as a valuable contribution to the field, addressing the challenges associated with limited labeled data and demonstrating its efficacy in improving classification performance. Figure 8: Accuracy (%) of SVHN. Exact numbers are provided in Table A3 (Appendix A). We also present in Figure 9 the test accuracy and test loss during the training process on the CIFAR-10 dataset with 1000 labeled samples using the different methods. It can be observed that MAC-SSL achieved rapid convergence, attaining the highest accuracy and nearly the lowest loss (with only a slight increase in loss towards the end).