Collection of generative models in Tensorflow
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Updated
Aug 8, 2022 - Python
Collection of generative models in Tensorflow
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
Tensorflow implementation of conditional variational auto-encoder for MNIST
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Conditional Variational AutoEncoder (CVAE) PyTorch implementation
Learning cell communication from spatial graphs of cells
Conditional out-of-distribution prediction
Learning informed sampling distributions and information gains for efficient exploration planning.
The official implementation of "Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting" presented in ECCV2022.
Implementation of a Convolutional Variational Autoencoder in Flux.jl
Code for our paper "VaPar Synth - A Variational Parametric Model for Audio Synthesis"
Implementation of the Conditional Variational Auto-Encoder (CVAE) in Tensorflow
PyTorch implementation of the conditional variational autoencoder (CVAE) from CodeSLAM
cVAE, VQ-VAE, VQ-VAE2, cVAE-cGAN, PixelCNN and Gated PixelCNN in tensorflow 2.x and keras
Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.
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