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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
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
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
Using a GAN to synthetically generate medical images for DL purposes
Conditional Generative Adversarial Network for generating synthetic faces with user specified attributes
Ancient coins reconstruction using CGANs
The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable
Using pix2pix and SinGAN to get into the movie
Image Generation using Generative Adversarial Networks (GANs) on MNIST dataset
cGAN_diff - more than pretty faces
SAGAN that conducted a CT noise reduction study based on conditional GAN
Code of model to generate images the fecades using pixtopix and cgans
Predicting strong gravitational lens wavelength information in JWST NIRcam imaging as observed by Euclid VIS/NISP