you have a lot of data and a lot more parameters so you can think of UDA as creating this noise at the input level using various different data augmentations and depending on the two modality for instance you use Auto Alcuin for images you would use TF idea word replacement or back translation for NLP and based on that you create different organizations at the same image or the same sample and enforces consistency laws and the unsupervised consistency but the last part and you also do supervised cross and freelance