MixMatch is a semi-supervised learning algorithm that unifies several dominant approaches to leverage unlabeled data. It combines data augmentation, label guessing, and consistency regularization to improve model performance. The algorithm first applies data augmentation to unlabeled examples and generates "guessed labels" by averaging predictions across multiple augmentations. These guessed labels are then used in a loss function that encourages the model to produce consistent predictions. MixMatch also incorporates MixUp, a technique that mixes labeled and unlabeled examples to improve generalization. The algorithm is evaluated on several standard image benchmarks, including CIFAR-10, CIFAR-100, SVHN, and STL-10, where it achieves state-of-the-art results. On CIFAR-10 with 250 labels, MixMatch reduces the error rate by a factor of 4 (from 38% to 11%), and on STL-10 by a factor of 2. The algorithm also demonstrates improved accuracy-privacy trade-offs for differential privacy. An ablation study shows that each component of MixMatch contributes to its success. MixMatch is implemented using a Wide ResNet-28 model and is effective in both semi-supervised learning and privacy-preserving learning scenarios. The results show that MixMatch outperforms other methods in most settings, often by a significant margin.MixMatch is a semi-supervised learning algorithm that unifies several dominant approaches to leverage unlabeled data. It combines data augmentation, label guessing, and consistency regularization to improve model performance. The algorithm first applies data augmentation to unlabeled examples and generates "guessed labels" by averaging predictions across multiple augmentations. These guessed labels are then used in a loss function that encourages the model to produce consistent predictions. MixMatch also incorporates MixUp, a technique that mixes labeled and unlabeled examples to improve generalization. The algorithm is evaluated on several standard image benchmarks, including CIFAR-10, CIFAR-100, SVHN, and STL-10, where it achieves state-of-the-art results. On CIFAR-10 with 250 labels, MixMatch reduces the error rate by a factor of 4 (from 38% to 11%), and on STL-10 by a factor of 2. The algorithm also demonstrates improved accuracy-privacy trade-offs for differential privacy. An ablation study shows that each component of MixMatch contributes to its success. MixMatch is implemented using a Wide ResNet-28 model and is effective in both semi-supervised learning and privacy-preserving learning scenarios. The results show that MixMatch outperforms other methods in most settings, often by a significant margin.