and easier over the training over the course of training and you get the problem of at some point you're gonna have to do actively sample hard negatives I think this paper just gets around it by having huge batch sizes so yeah but again they do get state-of-the-art on imagenet for these types of networks and augmentation strategies and they do look at how their loss appears to be more hyper parameter stable so if they change out the augmentation if they change the optimizer or the learning rate you can see here that the spread in accuracy is