ddbourgin/numpy-ml
Machine learning, in numpy
204 repositories
Machine learning, in numpy
Collection of generative models in Tensorflow
Collection of generative models in Pytorch version.
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
Awesome Generative Adversarial Networks with tensorflow
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking
Improved WGAN in Pytorch
Reimplementation of GANs
Simple Implementation of many GAN models with PyTorch.
Chainer implementation of recent GAN variants
Pytorch implementation of Wasserstein GANs with Gradient Penalty
Implementation of some different variants of GANs by tensorflow, Train the GAN in Google Cloud Colab, DCGAN, WGAN, WGAN-GP, LSGAN, SNGAN, RSGAN, RaSGAN, BEGAN, ACGAN, PGGAN, pix2pix, BigGAN
PyTorch implementation of DCGAN, WGAN-GP and SNGAN.
DCGAN LSGAN WGAN-GP DRAGAN PyTorch
implementation of several GANs with pytorch
GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.
A Tensorflow implementation of GAN, WGAN and WGAN with gradient penalty.
🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...
Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
A PyTorch implementation of SRGAN specific for Anime Super Resolution based on "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network". And another PyTorch WGAN-gp implementation of SRGAN referring to "Improved Training of Wasserstein GANs".
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
Repository for implementation of generative models with Tensorflow 1.x
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
Improved training of Wasserstein GANs
MalDataGen is an advanced Python framework for generating and evaluating synthetic tabular datasets using modern generative models, including diffusion and adversarial architectures.
TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.
Pytorch implementation of a Conditional WGAN with Gradient Penalty
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10