supervised learning updates the parameters of a neural network to match predicted class labels with the ground truth labels the construction of these ground truth class vectors is typically done with one hot encoding but other techniques such as label smoothing and knowledge distillation have been developed to overcome the limitations of one hot encoded ground truth class labels Mehta pseudo labels that uses the meta learning framework to dynamically adapt the target distribution or ground truth class labels throughout training