I understand that perturbations and generating new examples from labelled examples is a pretty normal park of the process when you only have a limited number of examples available.
Two different perturbations to the same image should have the same predicted label by the model, even if it doesn't know what the correct label is. That information can be used in the training.
As training progresses, the model will become better at labeled data. And so it can start predicting with high confidence on unlabeled images that are trivial/similar-looking/same distribution with labeled data. So, gradually unlabeled images get started being used as part of training. As training progresses, more and more unlabeled data are added.
The mathematics of the combined loss function and curriculum learning part talks about this.