midpoints here will give you a 50/50 mixture between the labels which basically means what you end up with is a line between this data and this data and it means that basically the network becomes more linear and the representations become more flat because flat is the optimal if you distribute our flat all the distances to the line are the same and this objective is optimized and this is basically my my kind of biggest problem with the method is that it it kind of mixes the input with a linear function where we know