just do k-means clustering on top of that that will give them on C for ten a thirty five point nine percent accuracy so not very good so the clustering you can't just cluster on top of these representations and then be done if they do what they say so this is sample and batch entropy loss this basically means you do not care about the nearest neighbors you do this entire thing but you only make an image close to the prediction close to itself and it's augmentations so you don't use any nearest neighbor information also