data mix-up and you see in this that the mix up is benefiting the performance of this model um going to the end uh concluding what we have understood from the paper or the basic purpose of what the authors wanted to showcase using all the components that they have in creating the algorithm it is seen that the hyper parameters that they've used is the augmentations the sharpening the mix and match and the loss that they've calculated it seems that they are have contributed quite well in the performance of the model