I think it's a fair claim. Labelled data is very hard to come by compared to unlabelled data. Being able to get a highly accurate model with only a small amount of labelled data is a very sought after and practical property.
Also the technique is quite novel: This is not pre-trained nets on labeled data, it is an unsupervised generative model.
Future research directions are exciting: Unsupervised prediction of the next frame in a video, and then being able to one-shot learn a wide range of visual tasks.
[0] https://www.youtube.com/watch?v=-yX1SYeDHbg&list=PLE6Wd9FR--...
[1] https://arxiv.org/abs/1412.6056 "Predicting Deeper into the Future of Semantic Segmentation"
This is what my NLP company (Luminoso) does -- we train a domain-general model of word meanings on a lot of data, then do the last step on the probably-small amount of specific data you actually have.
Even customers who are knowledgeable about machine learning usually haven't heard of the idea before. They've been assuming that the only way to do NLP is to get millions of labeled examples. Or to get a thousand labeled examples and put them into the kind of off-the-shelf algorithm that needs millions of labeled examples, which of course goes poorly.