Problem
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Refine synthetically simulated images to look real

https://machinelearning.apple.com/images/journals/gan/real_synt_refined_gaze.png

Approach
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* Generative adversarial networks

Contributions
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1. **Refiner** FCN that improves simulated image to realistically looking image
2. **Adversarial + Self regularization loss**
* **Adversarial loss** term = CNN that Classifies whether the image is refined or real
* **Self regularization** term = L1 distance of refiner produced image from simulated image. The distance can be either in pixel space or in feature space (to preserve gaze direction for example).


https://i.imgur.com/I4KxCzT.png

Datasets
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* grayscale eye images
* depth sensor hand images 


Technical Contributions
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1. **Local adversarial loss** - The discriminator is applied on image patches thus creating multiple "realness" metrices
https://machinelearning.apple.com/images/journals/gan/local-d.png

2. **Discriminator with history** - to avoid the refiner from going back to previously used refined images.

https://machinelearning.apple.com/images/journals/gan/history.gif