gan
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.
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Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"
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Aug 2, 2019 - MATLAB
Image to Image Translation Using Generative Adversarial Networks
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May 12, 2020 - MATLAB
A MATLAB implmentation of the StyleGAN generator
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Jun 19, 2020 - MATLAB
Implementation of "Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation"
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Apr 18, 2023 - MATLAB
NIPS 2018 "Invertibility of Convolutional Generative Networks from Partial Measurements"
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Mar 9, 2019 - MATLAB
GAN: An example for generating Gaussian distribution by a simple generating adversarial network.
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Dec 28, 2020 - MATLAB
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Aug 30, 2018 - MATLAB
Official Analysis Code Base for the Manifold Paper: Wang and Ponce, the Tuning Landscape of the Ventral Stream
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Sep 6, 2022 - MATLAB
Official implementation of "cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks" (IEEE TMM 2020)
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Aug 23, 2021 - MATLAB
Synthetic Data Generation by Very Basic 1-D GAN
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Aug 26, 2023 - MATLAB
Synthetic Data Generation (SDG) Using Vanilla GAN
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Jul 23, 2023 - MATLAB
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May 24, 2019 - MATLAB
Released June 10, 2014
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