from the paper where it says that the algorithm basically augments the unlabeled image and tries and classifies it based on the number of augmentations and then draws an average to guess the label of this unlabeled image that it has after it has this average getting predicted or the guess label getting predicted that less label gets sharpened uh what sharpening does is it basically moves the line of prediction or the line where the decisions are made away from the the higher density association of the data points