conclusions so in summary they introduced a method called UTA unsupervised state augmentation that used targeted data augmentation to generate diverse and realistic perturbations to input samples and so this enforces the model to be smooth the way they applied it with respect to these perturbations they also introduced TSA training signal annealing and this was to prevent UDA from overfitting when a lot more unlabeled data is available compared to the amount of labeled data and their results showed that they achieved state-of-the-art on