to adapt the ground truth class labels during training by using a teacher network to label data and then a student network that learns from those labels a quick overview of the meta pseudo labels algorithm is that a teacher model is trained along with a student model to set the students target distributions and adapt to the students learning state so typically these target distributions are these one hot encoded vectors where you might have like zero cat one dog and then zero for all of the other classes in the case of say C fart n so the idea