little you can improve your generalization even after interpolation and there's even more evidence that number of parameters are hurting if you plot the imagenet top one error over time then you see that in fact for these very very big networks the top one error is quite small it means be creasing here I mean this is cherry pick from this paper but there's clearly networks with 600 million parameters and it has passed they are very low so throwing in parameters in a non stupid way will not hurt your generalization I'm here okay