What to do with small set of labeled data and large set of unlabeled data? • /r/MachineLearning

We have a set of, say, 10K labeled images (two classes), and an unlabeled set that is maybe 10X larger (or even 100X, doesn't really matter for this discussion). What I'm wondering is can I train a NN on the initial labeled set of 10K images and then use that model to label a larger set of unlabeled images, and then use that larger set of labeled images to train the model again? Will this result in a better model? If so, does anyone have links to literature on this approach and what is called? Is this an example of semi-supervised learning?