semi-supervised learning and here they find that simply changing the augmentation strategy changes the error by more than a percent so you can just see this in context of of what's important here right they say again the ratio of unlabeled data seems pretty important we observe a significant decrease in error rates by losing using a large amounts of unlabeled data right then the optimizer and learning schedule seems to be very important as well in that they use this they say STD with momentum works much better than