Implementing a Conditional VAE for video prediction with PyTorch
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Updated
May 7, 2024 - Python
Implementing a Conditional VAE for video prediction with PyTorch
A Collection of Variational Autoencoders (VAE) in PyTorch.
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
A variational Autoencoder (VAE) to generate human faces based on the CelebA dataset. A VAE is a generative model that learns to represent high-dimensional data (like images) in a lower-dimensional latent space, and then generates new data from this space.
Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE
Handwritten Digit Generation with VAE and GAN are applied.
A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space
This repository contains the code, data and scripts used to write the Bachelor Thesis "Latent representations for traditional music analysis and generation".
Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.
A repository for generating synthetic data (images) using various DL/ML models.
The CNN implementation to qualify images. This repo also contains Japanese coin validation(with binaries) and MNIST challenge detection.
Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE
Variational Autoencoder (VAE) trained on MNIST
Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.
Tensorflow 2.x implementation of the beta-TCVAE (arXiv:1802.04942).
Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow
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