Conditional Generative Adversarial Networks (txt2img using conditional DCGAN).
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Updated
Jul 16, 2018 - Python
Conditional Generative Adversarial Networks (txt2img using conditional DCGAN).
Sampling from the solution of the Zakai equation, using the Signature and Conditional Wasserstein GANs
An attempt to convert Cartoon Sketches into Images using GAN
Tensorflow implementation of simple Conditional Generative Adversarial Network(CGAN).
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.
A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
Investigation into Generative Neural Networks.
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
Conditional Generation of MNIST images using conditional GAN in PyTorch 1.6.
A novel approach, named SamplerGAN, for generating high-quality labeled data
Implementation of different GANs using TensorFlow
PyTorch implementation of Conditional Generative Adversarial Networks (cGAN) for image colorization of the MS COCO dataset
Conditional GAN, Wasserstein distance and Gradient Penalty in tensorflow
Conditional GAN
PyTorch implementation of “Conditional Adversarial Camera Model Anonymization” (ECCV 2020 Advances in Image Manipulation Workshop)
Development of an image-based autonomous driving system for an e-FSAE.
NCTU(NYCU) Deep Learning and Practice Spring 2021
A template repository for GANs
Conditional Pokemon sprite generator - specify type(s), output your own unique pokemon!
Simulate experiments with the Conditional GAN architecture and training algorithm in PyTorch using this package.
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