stat946w18/AmbientGAN: Generative Models from Lossy Measurements: Difference between revisions
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https://openreview.net/ | = Introduction = | ||
Generative Adversarial Networks operate by simulating complex distributions but training them requires access to large amounts of high quality data. Ashish Bora, Eric Price and Alexandros G. Dimakis propose AmbientGAN as a way to recover the true underlying distribution from lossy data under certain conditions. Even when these conditions are not met the results from AmbientGAN still produce high quality results. | |||
= Problem Specification = | |||
= Model = | |||
AmbientGAN compared to GAN (AmbientGAN passes the generator output through a measurement function) | |||
= Results = | |||
= Open questions = | |||
= Criticisms = | |||
= References = | |||
https://openreview.net/forum?id=Hy7fDog0b | |||
Revision as of 02:08, 26 February 2018
Introduction
Generative Adversarial Networks operate by simulating complex distributions but training them requires access to large amounts of high quality data. Ashish Bora, Eric Price and Alexandros G. Dimakis propose AmbientGAN as a way to recover the true underlying distribution from lossy data under certain conditions. Even when these conditions are not met the results from AmbientGAN still produce high quality results.
Problem Specification
Model
AmbientGAN compared to GAN (AmbientGAN passes the generator output through a measurement function)