Efficient semi-supervised learning via multi-augmentations on single samples for edge AI training

Abstract

In this study, we propose a new semi-supervised learning (SSL) method that achieves accuracy comparable to FixMatch with a smaller batch size. Our method generates multiple strong augmentations from a single unlabeled data point and applies Mixup regularization to enhance training stability. We also prioritize effective data augmentation algorithms. We evaluated our method by comparing its accuracy and computation time to FixMatch, finding that generating five strong augmentations from a single unlabeled data point provided the highest accuracy of 94.13% and reduced computation time to 70.8% of FixMatch. Additionally, our method outperformed other SSL methods on CIFAR-10, CIFAR-100, SVHN, and STL-10, especially with fewer labeled samples. An ablation study confirmed that both Mixup regularization and Prioritized Strong Augmentations contribute to improved accuracy and stability. Our method thus achieves comparable accuracy to FixMatch while reducing computation time.