A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
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Updated
Mar 5, 2017 - Python
A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder
TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)
Python toolbox for solving imaging continuous optimization problems.
Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion
Experiments on Disentangled Representation Learning using Variational autoencoding algorithms
Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)
Disentangling the latent space of a VAE.
automatic/analytical differentiation benchmark
Discrete Variational Autoencoder in PyTorch
[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".
Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)
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