An attempt to convert Cartoon Sketches into Images using GAN
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Updated
Jan 7, 2021 - Python
An attempt to convert Cartoon Sketches into Images using GAN
Conditional Generative Adversarial Networks (txt2img using conditional DCGAN).
Estimating brain activity for a stimulus as measured by fMRI using a volumetric conditional Generative Adversarial Network (GAN) model.
It uses Conditional GAN(Generative adversarial networks) to convert a front face image into a more primitive representation of the face.
Sampling from the solution of the Zakai equation, using the Signature and Conditional Wasserstein GANs
Tensorflow implementation of simple Conditional Generative Adversarial Network(CGAN).
CS565600
During my studies I had a lot of trouble finding a cDCGAN architecture that worked as I expected, so I decided to write my own version, finding an alternative way to condition it.
This repo contains code for enhancing a degraded image using CGAN
Generative Modeling using GANs, DCGANs, WGAN, WGAN-GP, ProGAN, Conditional GANs and Controllable Generation.
A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
Conditional Generation of MNIST images using conditional GAN in PyTorch 1.6.
Implementation of different GANs using TensorFlow
A novel approach, named SamplerGAN, for generating high-quality labeled data
Investigation into Generative Neural Networks.
Logistic regression, deep learning, YOLO, Recursive Neural Networks, GAN and Conditional GAN
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