in which we'll have a single training and there are also areas like label consistency which is make sure that the augmentations of the sample have the same class so you have an image we know that we use a lot of the documentation and regular supervised learning but in semi-supervised learning you have a lot of unlabeled data and you can't apply the augmentation to them if you're not passing them to the classifier but instead what you can do is you take an unlabeled sample it create two different orientations of it