Learning from Simulated and Unsupervised Images through Adversarial Training Learning from Simulated and Unsupervised Images through Adversarial Training
Paper summary Problem -------------- Refine synthetically simulated images to look real https://machinelearning.apple.com/images/journals/gan/real_synt_refined_gaze.png Approach -------------- * Generative adversarial networks Contributions ---------- 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 ------------ * grayscale eye images * depth sensor hand images Technical Contributions ------------------------------- 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
arxiv.org
scholar.google.com
Learning from Simulated and Unsupervised Images through Adversarial Training
Shrivastava, Ashish and Pfister, Tomas and Tuzel, Oncel and Susskind, Josh and Wang, Wenda and Webb, Russell
arXiv e-Print archive - 2016 via Local Bibsonomy
Keywords: dblp


Summary by Kirill Pevzner 8 months ago
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