The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python.
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Updated
Apr 12, 2024 - Jupyter Notebook
The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python.
The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met
The following study, through which we can generate X-ray images of the chest region in a semi-conditional manner, by taking advantage of the probability distributions.
Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra.
Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Beginner's Guide to building GAN from scratch with Tensorflow and Keras
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
My TensorFlow/Keras implementation of InfoGAN
Simple Implementation of many GAN models with PyTorch.
A novel approach, named SamplerGAN, for generating high-quality labeled data
Repository for my research on generative modelling of cell images
PyTorch Implementation of InfoGAN
Collection of generative models in Tensorflow
Implementation of InfoGAN using PyTorch lightning
Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
PyTorch implementations of Generative Adversarial Network series
PyTorch implementation of InfoGAN
Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE
Quick Tensorflow 2.0 implementation of InfoGAN trained on MNIST.
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