unlabeled data it's a useful bit a regularizer classifier the second idea is your labeling where you take your classifier you asked to classify to predict what the labels are for unlabeled here and you take the confident directions and convert them to extra they ask 'but was the ground truth and train the model on those data points so this idea is also referred to literature as sub training the area of training the model of its own predictions if the model is confident enough and expanding your data set and regularizing your model further