retraining direction very calculated gradient in approximate fashion next you have this thing called unsupervised consistency loss which is you take the noise from the model and your original model and you make sure that the logic seriously similar unlabeled data so this is something you already know and a final loss is combination of supervised and unsupervised consistency loss and you can see that virtual adversity of training actually works pretty well so this is a green eyes illustration of which I do sell training