the different kinds of noises like grappa stochastic depth and data augmentation you get the best possible numbers so in summary we looked at semi-square is learning and it's a practically important problem in the industry for two different scenarios one is when you have a lot of label data and a lot more on label data like for instance image ladder and j ft and you're trying to improve the performance of image net the other is when you have very little label data and you have plenty of unequal data which is usually