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
GAN: An example for generating Gaussian distribution by a simple generating adversarial network.
[FG 2019 Oral] Attribute-Guided Sketch Generation
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Inpainting and upscaling images of handwritten eights using a pretrained generator model
Released June 10, 2014