Oleg Zabluda's blog
Friday, July 13, 2018
 
AmbientGAN: Generative models from lossy measurements (2018) Ashish Bora, Eric Price, Alexandros G. Dimakis
AmbientGAN: Generative models from lossy measurements (2018) Ashish Bora, Eric Price, Alexandros G. Dimakis
"""
We consider the task of learning an implicit generative model given only lossy measurements of samples from the distribution of interest. We show that the true underlying distribution can be provably recovered even in the presence of per-sample information loss for a class of measurement models. Based on this, we propose a new method of training Generative Adversarial Networks (GANs) which we call AmbientGAN. On three benchmark datasets, and for various measurement models, we demonstrate substantial qualitative and quantitative improvements. Generative models trained with our method can obtain 2-4x higher inception scores than the baselines.

TL;DR: How to learn GANs from noisy, distorted, partial observations
"""
https://openreview.net/forum?id=Hy7fDog0b
https://openreview.net/forum?id=Hy7fDog0b

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