Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)
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Updated
Oct 26, 2019 - Python
Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)
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)
Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)
Disentangling the latent space of a VAE.
Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion
automatic/analytical differentiation benchmark
[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".
A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
Python toolbox for solving imaging continuous optimization problems.
Discrete Variational Autoencoder in PyTorch
Experiments on Disentangled Representation Learning using Variational autoencoding algorithms
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