budget which is you should spend equal amount of time tuning hyper parameters for all of them and if your unlabeled data is coming from a distribution that is a necessary overlap with your label data points then the benefits of something surprise learning will not be there thirdly most methods that likelike don't work well in a very very low data regime so this is not true right now but this was true when that paper was published and we look see how it changed over time and transferring 320 machine had produce better error rates but again