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

Using cGANs to remove objects from a photo

Implementation of Conditional Generative Adversarial Networks in PyTorch

[ICML 2022] Conditional GANs with Auxiliary Discriminative Classifier

Using a GAN to synthetically generate medical images for DL purposes

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

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

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

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

Enhancement and Segmentation GAN

A Tensorflow 2 implementation of SNGAN and Projection Discriminator

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

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

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.

Ancient coins reconstruction using CGANs

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

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

Predicting strong gravitational lens wavelength information in JWST NIRcam imaging as observed by Euclid VIS/NISP