to the teacher that is not noised when it generates the pseudo labels in our experiments we use two types of noise input noise and model noise all right first data augmentation is an important noising method in noisy student training because it forces the student to ensure prediction consistency across augmented versions of an image specifically in our method the teacher produces high quality pseudo labels by reading in clean images while the student is required to produce to reproduce those labels with augmented images as an input