atreyasha / Deep Generative Models
Licence: mit
Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)
Stars: ✭ 34
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Deep Generative Models
haskell-vae
Learning about Haskell with Variational Autoencoders
Stars: ✭ 18 (-47.06%)
Mutual labels: mnist, variational-autoencoder
Textbox
TextBox is an open-source library for building text generation system.
Stars: ✭ 257 (+655.88%)
Mutual labels: generative-adversarial-network, variational-autoencoder
lagvae
Lagrangian VAE
Stars: ✭ 27 (-20.59%)
Mutual labels: generative-adversarial-network, variational-autoencoder
gan-vae-pretrained-pytorch
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Stars: ✭ 134 (+294.12%)
Mutual labels: generative-adversarial-network, mnist
Tensorflow Generative Model Collections
Collection of generative models in Tensorflow
Stars: ✭ 3,785 (+11032.35%)
Mutual labels: mnist, variational-autoencoder
vaegan
An implementation of VAEGAN (variational autoencoder + generative adversarial network).
Stars: ✭ 88 (+158.82%)
Mutual labels: generative-adversarial-network, variational-autoencoder
MNIST-invert-color
Invert the color of MNIST images with PyTorch
Stars: ✭ 13 (-61.76%)
Mutual labels: generative-adversarial-network, mnist
Generative Continual Learning
No description or website provided.
Stars: ✭ 51 (+50%)
Mutual labels: generative-adversarial-network, variational-autoencoder
Pytorch Mnist Celeba Gan Dcgan
Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
Stars: ✭ 363 (+967.65%)
Mutual labels: generative-adversarial-network, mnist
Pytorch Mnist Celeba Cgan Cdcgan
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
Stars: ✭ 290 (+752.94%)
Mutual labels: generative-adversarial-network, mnist
playing with vae
Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST
Stars: ✭ 53 (+55.88%)
Mutual labels: mnist, variational-autoencoder
Disentangling Vae
Experiments for understanding disentanglement in VAE latent representations
Stars: ✭ 398 (+1070.59%)
Mutual labels: mnist, variational-autoencoder
DCGAN-Pytorch
A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"
Stars: ✭ 23 (-32.35%)
Mutual labels: generative-adversarial-network, mnist
VAE-Gumbel-Softmax
An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
Stars: ✭ 66 (+94.12%)
Mutual labels: mnist, variational-autoencoder
gans-2.0
Generative Adversarial Networks in TensorFlow 2.0
Stars: ✭ 76 (+123.53%)
Mutual labels: generative-adversarial-network, mnist
tt-vae-gan
Timbre transfer with variational autoencoding and cycle-consistent adversarial networks. Able to transfer the timbre of an audio source to that of another.
Stars: ✭ 37 (+8.82%)
Mutual labels: generative-adversarial-network, variational-autoencoder
precision-recall-distributions
Assessing Generative Models via Precision and Recall (official repository)
Stars: ✭ 80 (+135.29%)
Mutual labels: generative-adversarial-network, variational-autoencoder
deep-blueberry
If you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!
Stars: ✭ 17 (-50%)
Mutual labels: generative-adversarial-network, variational-autoencoder
Generative models tutorial with demo
Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
Stars: ✭ 276 (+711.76%)
Mutual labels: generative-adversarial-network, variational-autoencoder
Pytorch Rl
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Stars: ✭ 394 (+1058.82%)
Mutual labels: generative-adversarial-network, variational-autoencoder
Deep Generative Models using TensorFlow 2.0
This repository contains TensorFlow 2.0 python source-code for deep generative models. This repository prioritizes using low-level tensorflow implementations where possible. The intended purpose is to allow for a more in-depth understanding of corresponding algorithms.
Note: Source code in this repository was only tested on the CPU. Therefore extending usage to GPU(s) might result in issues.
Dependencies
This repository's source code was tested with Python versions 3.7.*.
-
Install python dependencies located in
requirements.txt:$ pip install -r requirements.txt
-
Optional: To develop this repository, it is recommended to initialize a pre-commit hook for automatic updates of python dependencies:
$ ./init.sh
Workflow
Information regarding execution of python scripts can be found in the readme in the /src directory.
Author
Atreya Shankar, Cognitive Systems 2018
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].