compared to the 97 percent that you get with like 50,000 images that is pretty pretty cool right simply by having all other images not labeled but pseudo labeled and consistency regularized right so the the two to two things that are combined by fixed match again or consistency regularization which basically it means that the model should output similar predictions when fed perturbed versions of the same image right this they they're really forthcoming that they are not the ones who invented this they just combine the