Tensorflow implementation of conditional variational auto-encoder for MNIST
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Updated
Apr 25, 2017 - Python
Tensorflow implementation of conditional variational auto-encoder for MNIST
Implementation of the Conditional Variational Auto-Encoder (CVAE) in Tensorflow
The implementation of Gumbel softmax reparametrization trick for discrete VAE
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
A PyTorch implementation of neural dialogue system using conditional variational autoencoder (CVAE)
Implementations of deep learning algorithms
VAE and CVAE pytorch implement based on MNIST
a collection of variational autoencoders
Code for our paper "VaPar Synth - A Variational Parametric Model for Audio Synthesis"
Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.
Bayesian based machine learning implementations (GMM, VAE & conditional VAE).
CVAE implementation on MNIST dataset using PyTorch
Мой проект курса Deep Learning School, посвященный архитектуре Autoencoder и применение её в обработке изображений.
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