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
Implementation of Conditional Generative Adversarial Networks in PyTorch
Using cGANs to remove objects from a photo
Enhancement and Segmentation GAN
A Tensorflow 2 implementation of SNGAN and Projection Discriminator
TensorFlow implementation of Conditional Generative Adversarial Nets (CGAN) with MNIST dataset.
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.
Conditional Generative Adversarial Network for generating synthetic faces with user specified attributes
Source code and pretrained models for pix2pix - Inference on image and paint using pyqt5
Using pix2pix and SinGAN to get into the movie
Ancient coins reconstruction using CGANs
SAGAN that conducted a CT noise reduction study based on conditional GAN
Using a GAN to synthetically generate medical images for DL purposes
The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN
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