you train with the classic method erm the these errors happen much more often that's exactly the situation I pointed out at the beginning where in the high dimensions it can you know occur that all sorts of decision boundary sneak here in between the two data points and by interpolating between them during training you sort of much reduce that you reduce that effect a lot now this they also say that the gradient norm of the gradients of the model with respect to input in between training data it happens the same thing the norm