so that when the loss is calculated on the unlabeled data it it is not affecting the the final prediction after sharpening what remains is two sets of data one is the augmented label data with the sharpened predictions and the label data which already has its own labels so what is done is a mix-up based on this parameter tuning which is called alpha so what basically makes up does is let's say uh you have an image of a tiger and a cheetah and you want to see how much of one image corresponds to a tiger and how much one image