the idea is when you apply some of these state-of-the-art data augmentation methods the the output augment example it is still very much in in the same distribution as the the sample it was transformed from so so these are sort of realistic augment augmented examples whereas when you just apply some random Gaussian noise it can often make the the data point if you apply too much make it sort of very unrecognizable and not realistic so so not really as we say valid it also applies a diverse perturbation so again if you want to use