it basically means that we need to reduce the randomness in the prediction of the unlabeled data or the classified decision boundary should not pass through the high density region of the margin marginal data distribution this is done by outputting low entropy prediction on the unlabeled data so mix match also implicitly achieves entropy minimization by adding a loss function and using a sharpening function on the target distribution for unlabeled data the traditional regularization is again a method applied to avoid overfitting