learning and the second one asapna mentioned is the l2 loss for the unlabeled learning i would stop here and ask if there were any questions okay i go forward so this slide explains what i just talked about but in a more um direct way and what all they have tuned in so the key factors to keep in mind for this algorithm to perform better or worse is tuning these hybrid parameters as the augmentations sorry k i'm sorry then the the sharpening temperature t the the parameters for mix up that's alpha and the weight of the unsupervised