outputs a distribution like this both are class 1 labels but one is much more confident than the other so what you want to do is you want to filter out these low confidence labels because you know the model isn't really sure but it has to assign a class but that's usually an indication that it is an out of domain image so if they filter this it works better and then also to ensure that the distribution of the unlabeled images match that of the training set we also need to balance the number of unlabeled images for each class as all