architectures so here's a nice way to understand UDA so think about label data and you have X and your ground through the Y star and your training a classifier at P feet of Y given X and you have your standard supervised cross-entropy laws that make sure that the logit so the true class are really maximized and you also have unlabeled data so this is the situations and it's quite funny and models like virtual address zero training add noise to regularize to models predictions and the noises between the virtual address