Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification
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Updated
Sep 11, 2016 - Python
Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification
Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)
Transfer learning for flight-delay prediction via variational autoencoders in Keras
A Keras/TensorFlow-based implementation of Adversarial Variational Bayes by L. Mescheder et al.
UofU GANS
A PyTorch implementation of alpha-GAN
Data and Trained models can be downloaded from https://goo.gl/7PrKD2
Presentation about Autoencoders for Seoul AI Meetup on July 8, 2017.
PyTorch Implementations of Generative models
MXNet/Gluon implementation of the original (Gaussian) Variational Autoencoders (VAE)
This repository tries to provide unsupervised deep learning models with Pytorch
A tensorflow implementation of "Generating Sentences from a Continuous Space"
TensorFlow implementation of Auto-Encoding Variational Bayes.
Inversion with a VAE-based low-dimensional parameterization for complex geologic priors (Python 2.7)
This is the implementation of variational autoencoders (VAE) written in Python 3.6 with Keras.
Code to replicate results from the VAE workshop at ODSC 2018 Kyiv. It's still work in progress
Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling
A variational autoencoder model trained on the MNIST dataset using Tensorflow's Eager Execution
Implementation of different approaches to train Discrete Variational Autoencoders
Variational Autoencoding for Radio Galaxies
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