[rkaplan]

This paper is important for a few reasons:

- Labeled data is very expensive. Historically attempts to learn on synthetic data has failed because ConvNets are very good at detecting small visual artifacts in the synthetic data and using those for classification during training. At test time on real data, those artifacts aren't present so model fails. A technique that can beat state-of-the-art (admittedly on a very narrow Eye Gaze dataset, but still) by only training on labels from synthetic data and testing on real data is important.

- They present a useful new idea to improve GAN training: using a history of "fake" images, rather than only the latest fake images from the generator. Ask anyone who has tried to train a GAN: the training is really unstable, each network only cares about beating the latest version of its "opponent". They show good improvements by saving many previous fake outputs to make the generator more robust. This reminds me of Experience Replay from DeepMind for RL.

- It's a published paper from Apple! Great that they are starting to contribute back to the research community.