Image Generation using Generative Adversarial Networks (GANs) on MNIST dataset
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Updated
Dec 17, 2017 - Jupyter Notebook
Image Generation using Generative Adversarial Networks (GANs) on MNIST dataset
Predicting strong gravitational lens wavelength information in JWST NIRcam imaging as observed by Euclid VIS/NISP
Using pix2pix and SinGAN to get into the movie
The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN
Conditional Generative Adversarial Network for generating synthetic faces with user specified attributes
SAGAN that conducted a CT noise reduction study based on conditional GAN
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable
Ancient coins reconstruction using CGANs
PyTorch implementation of 'Pix2Pix' (Isola et al., 2017) and training it on Facades and Google Maps
A Tensorflow 2 implementation of SNGAN and Projection Discriminator
Enhancement and Segmentation GAN
Using a GAN to synthetically generate medical images for DL purposes
TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.
PANDA (Pytorch) pipeline, is a computational toolbox (MATLAB + pytorch) for generating PET navigators using Generative Adversarial networks.
Conditional Generative Adversarial Networks(cgans) to convert text to image implemented in Python and TensorFlow & Keras
Source code and pretrained models for pix2pix - Inference on image and paint using pyqt5
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