wuga214 / Implementation_variational Auto Encoder
Licence: mit
Simple implementation of Variational Autoencoder
Stars: ✭ 81
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Implementation variational Auto Encoder
Tensorflow Generative Model Collections
Collection of generative models in Tensorflow
Stars: ✭ 3,785 (+4572.84%)
Mutual labels: variational-autoencoder
Li emnlp 2017
Deep Recurrent Generative Decoder for Abstractive Text Summarization in DyNet
Stars: ✭ 56 (-30.86%)
Mutual labels: variational-autoencoder
Pytorch Rl
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Stars: ✭ 394 (+386.42%)
Mutual labels: variational-autoencoder
Scvi Tools
Deep probabilistic analysis of single-cell omics data
Stars: ✭ 452 (+458.02%)
Mutual labels: variational-autoencoder
Variational Autoencoder
PyTorch implementation of "Auto-Encoding Variational Bayes"
Stars: ✭ 25 (-69.14%)
Mutual labels: 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 (+240.74%)
Mutual labels: variational-autoencoder
Codeslam
Implementation of CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM paper (https://arxiv.org/pdf/1804.00874.pdf)
Stars: ✭ 64 (-20.99%)
Mutual labels: variational-autoencoder
Continual Learning
PyTorch implementation of various methods for continual learning (XdG, EWC, online EWC, SI, LwF, GR, GR+distill, RtF, ER, A-GEM, iCaRL).
Stars: ✭ 600 (+640.74%)
Mutual labels: variational-autoencoder
Variationaldeepsemantichashing
The original implementation of the models and experiments of Variational Deep Semantic Hashing paper (SIGIR 2017)
Stars: ✭ 50 (-38.27%)
Mutual labels: variational-autoencoder
Disentangling Vae
Experiments for understanding disentanglement in VAE latent representations
Stars: ✭ 398 (+391.36%)
Mutual labels: variational-autoencoder
Tensorflow Mnist Vae
Tensorflow implementation of variational auto-encoder for MNIST
Stars: ✭ 422 (+420.99%)
Mutual labels: variational-autoencoder
Simple Variational Autoencoder
A VAE written entirely in Numpy/Cupy
Stars: ✭ 20 (-75.31%)
Mutual labels: variational-autoencoder
Vae cf
Variational autoencoders for collaborative filtering
Stars: ✭ 386 (+376.54%)
Mutual labels: variational-autoencoder
Vae protein function
Protein function prediction using a variational autoencoder
Stars: ✭ 57 (-29.63%)
Mutual labels: variational-autoencoder
Neuraldialog Cvae
Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
Stars: ✭ 279 (+244.44%)
Mutual labels: variational-autoencoder
Variational Autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Stars: ✭ 807 (+896.3%)
Mutual labels: variational-autoencoder
Bayesian Machine Learning
Notebooks about Bayesian methods for machine learning
Stars: ✭ 1,202 (+1383.95%)
Mutual labels: variational-autoencoder
Repo 2017
Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano
Stars: ✭ 1,123 (+1286.42%)
Mutual labels: variational-autoencoder
Deep Generative Models
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 (-58.02%)
Mutual labels: variational-autoencoder
Variational Autoencoder
This is a enhanced implementation of Variational Autoencoder. Both fully connected and convolutional encoder/decoder are built in this model. Please star if you like this implementation.
Use
$python vae_train_amine.py # for training
$python sample.py # for sampling
Update
- Removed standard derivation learning on Gaussian observation decoder.
- Set the standard derivation of observation to hyper-parameter.
- Add deconvolution CNN support for the Anime dataset.
- Remove Anime dataset itself to avoid legal issues.
Pre-Trained Models
There are two pretrained models
- Anime
- MNIST
The weights of pretrained models are locaded in weights folder
Samples
ANIME
MNIST
Latent Space Distribution
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].