Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Implementation of Conditional Generative Adversarial Networks in PyTorch

Using cGANs to remove objects from a photo

[ICML 2022] Conditional GANs with Auxiliary Discriminative Classifier

Source code and pretrained models for pix2pix - Inference on image and paint using pyqt5

Conditional Generative Adversarial Networks(cgans) to convert text to image implemented in Python and TensorFlow & Keras

Using a GAN to synthetically generate medical images for DL purposes

PANDA (Pytorch) pipeline, is a computational toolbox (MATLAB + pytorch) for generating PET navigators using Generative Adversarial networks.

TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.

A Tensorflow 2 implementation of SNGAN and Projection Discriminator

Enhancement and Segmentation GAN

PyTorch implementation of 'Pix2Pix' (Isola et al., 2017) and training it on 'Facades' and Google Maps

Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable

The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN

Conditional Generative Adversarial Networks (CGANs) extend the capabilities of traditional GANs by conditioning both the generator and discriminator models on additional information, typically class labels or other forms of auxiliary information.

Conditional Generative Adversarial Network for generating synthetic faces with user specified attributes

SAGAN that conducted a CT noise reduction study based on conditional GAN

Ancient coins reconstruction using CGANs