the car never says it doesn't right the car responds now saying the most efficient way to do it though is to equalize the frequencies of the categories of samples during training this forces the network to pay attention to all the relevant features for all the sample categories and training with an l1 instead of an l2 will not even begin to solve the problem I would pretty much argue training with an l1 loss here would exacerbate the problem because the l2 loss is much more sensitive to outliers drawl Sutton says serious