MixMatch

In FRDC-ML, we leverage semi-supervised learning to improve the model's performance through better augmentation consistency and using even unlabelled data.

The algorithm we use is MixMatch. A state-of-the-art semi-supervised learning algorithm. It is based on the idea of consistency regularization, which encourages models to predict the same class even after augmentations that occur naturally in the real world.

Our implementation of MixMatch is a refactored version of YU1ut/MixMatch-pytorch We've refactored the code to follow more modern PyTorch practices, allowing us to utilize it with modern PyTorch frameworks such as PyTorch Lightning.

We won't go through the details of MixMatch here, see Our Documentation in our MixMatch-PyTorch-CIFAR10 repository for more details.