are two components here right there is first the supervised loss this is the important one ultimately because we have the true labels right and then second there is the unsupervised loss which is just an auxiliary loss that is supposed to just kind of tune our model to the nature of the data right so don't forget that this this down here just concerns the unsupervised part of that loss so if you think what does the model actually learn when whenever you train it like this it basically learns to revert this strong augmentation right say basically