semi-supervised approach wherein you have iterations where you take in from your unlabeled set perform some basic transformations or have a have some sharpening done for your confidence uh of your label set having say uh prediction probability or maybe have co-training methods that have two classifiers learning on the same view etc and then give your prediction so mix match as such introduces all of these methods together so they they didn't stop at just using simple uh transformations or simple decision boundary making algorithms