859 Open source projects that are alternatives of or similar to Tensorflow Generative Model Collections

Collection of generative models in Pytorch version.

Playing with MNIST. Machine Learning. Generative Models.

Repository for implementation of generative models with Tensorflow 1.x

Generative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)

Simple Implementation of many GAN models with PyTorch.

GANs Implementations in Keras

wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

A Tensorflow implementation of GAN, WGAN and WGAN with gradient penalty.

Assessing Generative Models via Precision and Recall (official repository)

Implemented Variational Autoencoder generative model in Keras for image generation and its latent space visualization on MNIST and CIFAR10 datasets

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

Experiments for understanding disentanglement in VAE latent representations

でぃーぷらーにんぐを無限にやってディープラーニングでDeepLearningするための実装CheatSheet

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.

Tensorflow implementation of conditional variational auto-encoder for MNIST

Protein function prediction using a variational autoencoder

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.

Awesome Generative Adversarial Networks with tensorflow

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Comparison of Generative Models in Tensorflow

Pytorch implementation of pix2pix for various datasets.

Periodic Spatial Generative Adversarial Networks

Tensorflow implementation of different GANs and their comparisions

Speech Enhancement Generative Adversarial Network in TensorFlow

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

A MNIST-like fashion product database. Benchmark 👇

Stochastic Adversarial Video Prediction

Invert the color of MNIST images with PyTorch

Face generator from sketches using cGAN (pix2pix) model

Tensorflow implementation of variational auto-encoder for MNIST

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.

Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images

Implementation of a convolutional Variational-Autoencoder model in pytorch.

No description or website provided.

Generative Adversarial Networks in TensorFlow 2.0

Tensorflow implementation of adversarial auto-encoder for MNIST

PyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"

Torch Implemention of ECCV'16 paper: Attribute2Image

IPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Pure tensorflow implementation of progressive growing of GANs

Text Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

Invariant representation learning from imaging and spectral data

PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)

여러가지 유명한 신경망 모델들을 제공합니다. (DCGAN, VAE, Resnet 등등)

Tensorflow Implementation of Paper "Improved Training of Wasserstein GANs"

PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

There are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.

Implementation of VAE and Style-GAN Architecture Achieving State of the Art Reconstruction

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets