Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"
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Updated
Aug 2, 2019 - MATLAB
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"
Image to Image Translation Using Generative Adversarial Networks
Implementation of "Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation"
NIPS 2018 "Invertibility of Convolutional Generative Networks from Partial Measurements"
A MATLAB implmentation of the StyleGAN generator
GAN: An example for generating Gaussian distribution by a simple generating adversarial network.
Official implementation of "cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks" (IEEE TMM 2020)
Synthetic Data Generation by Very Basic 1-D GAN
Synthetic Data Generation (SDG) Using Vanilla GAN
Official Analysis Code Base for the Manifold Paper: Wang and Ponce, the Tuning Landscape of the Ventral Stream
Released June 10, 2014