At the PyData DE I just saw an excellent talk about GANs and data augmentation in image recognition:

https://www.slideshare.net/FlorianWilhelm2/performance-evalu...

The authors were able to outperform Google ML by a large margin for a vision task that involved recognizing numbers from car registration documents. With just 160 manually collected training samples they were able to train a neural net that could recognize characters with 99.7 % accuracy. GoogleML performed very poorly in comparison, which I found very surprising because it didn't seem to be such a hard recognition task (clean, machine-written characters on a structured, green background).