# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 3 Results
### Implementation Details  
1. Datasets and metrics: MAC-SSL was experimentally validated on the CIFAR-10 [42], CIFAR-100 [42], and SVHN [43] datasets, as shown in Figure 5.  
* CIFAR-10: The CIFAR-10 dataset is a widely used benchmark in the field of computer vision. It consists of 60,000 color images, each sized at 32 \(\times\) 32 pixels, belonging to 10 different classes. These classes include common objects such as airplanes, automobiles, cats, birds, dogs, and more. The dataset is divided into 50,000 training images and 10,000 test images, with an equal distribution of images across the classes.  
* CIFAR-100: Similar to CIFAR-10, CIFAR-100 is another dataset used for image classification tasks. It contains 60,000 images, each also sized at 32 \(\times\) 32 pixels, but it is divided into 100 fine-grained classes. Each class represents a specific object or concept, such as insects, food containers, trees, fish, and so on. The dataset is split into 50,000 training images and 10,000 test images, with a balanced distribution of images across the classes. Classifying CIFAR-100 is more difficult than CIFAR-10 due to its larger number of categories.
* SVHN (Street View House Numbers): The SVHN dataset is focused on digit recognition tasks. It consists of real-world images taken from Google Street View that contain house numbers. Each image in SVHN contains multiple digits (from zero to nine). The images are of varying sizes but are predominantly sized at 32 \(\times\) 32 pixels. SVHN is divided into a training set with 73,257 images and a test set with 26,032 images.
2. Experiment setting: All experiments were implemented using PyTorch and conducted on an Ubuntu system server with four NVIDIA 3090 GPUs and 128 GB of memory, and they followed SSL evaluation methods. The experiments were conducted using the \"Wide ResNet-28\" model from [44]. Training for the CIFAR-10 [42] and SVHN [43] datasets continued for 300 epochs until convergence. A batch size of 64 was used with the Wide ResNet-28-2 model, which has 1.47 M parameters. Due to computational limitations, a batch size of up to 32 was used for the CIFAR-100 [42] dataset in MAC-SSL, while for the other methods, the batch size remained at 64. The Wide ResNet-28-8 model with 23.46 M parameters was utilized for the CIFAR-100 training. For the selection of hyperparameters, we employed a random search. The hyperparameter \(\mu\), controlling the sample ratio, was set to 5. The weight hyperparameter \(\lambda_{u}\) was set as \(\frac{currentepoch}{totalepoch}\times 75\), and \(\lambda_{c}\) was set to 1. The learning rate was set to 0.01 for CIFAR-10, CIFAR-100, and SVHN. The threshold \(\tau\) was set to 0.95. The training employed the SGD optimizer with cosine weight decay. Exponential moving average (EMA) with a decay rate of 0.999 was utilized for evaluating the models. Of note, for SVHN, we applied strong augmentation using RandAugment [40] on top of moderate augmentation, referred to as MAC-SSL-II. This differed from CIFAR-10/100. Each epoch involved 1024 steps of training, and checkpoints were saved at each epoch. The average accuracy of the last 20 checkpoints was recorded. This approach simplified the analysis process.  
For the SOTA algorithms used in the comparative experiments, we reproduced results by obtaining their source code and maintaining the original settings mentioned in the code. For some datasets that were not included in their original code, we added them ourselves and used the hyperparameters specified in the respective papers. The source code for the  
Figure 5: (**a**) CIFAR-10 dataset. (**b**) CIFAR-100 dataset. (**c**) SVHN dataset.  
comparative algorithms can be downloaded from the authors' or reproducers' homepages, except for Mean-Teacher [32] (reproduced by ourselves). The codes for CoMatch1[34], MixMatch2[33], ICT3[45], VAT3[46], Temporal-ensembling3[30], and Pimodel3[30] are available on their respective authors' or reproducers' homepages.