outputs for different decoding so the key idea and UDA or unsprayed augmentation is apply the state-of-the-art data augmentation techniques on unlabeled data for consistency training in semi-supervised learning you've already seen that PI model are like like Supai model was basically doing consistency training but it was a pretty old paper and data augmentation through these numeral architectures were not as devil back then so you can think of UTA is doing pi model right by using a lot of data augmentations on the right