Simple VAE face generator
-
Updated
Jul 26, 2023 - Jupyter Notebook
Simple VAE face generator
A simple implementation of variational Auto encoders using Mnist dataset in tensorflow.
Built a model to create highlights/summary of given video. The results of this study shows that, with a remarkable similarity index(SSIM) of 98%, the recommended technique is quite successful in choosing keyframes that are both educational and distinctive from the original movie
A PyTorch implementation of multimodal VRNN and VAE.
Testing the Reproducibility of the paper: MixSeq. Under the assumption that macroscopic time series follow a mixture distribution, they hypothesise that lower variance of constituting latent mixture components could improve the estimation of macroscopic time series.
Running VAEs on mobile and IOT devices using TFLite.
Leveraging the power of LD variational autoencoders to identify latent representations as dim red embeddings of sc data
A repository for generating synthetic data (images) using various DL/ML models.
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.
Implementing a Conditional VAE for video prediction with PyTorch
Variational Autoencoder (VAE) trained on MNIST
ColorVAE is a Vanilla Auto Encoder (V.A.E.) which can be used to add colours to black and white images.
This repo is devoted to the pracicals of the course Deep Learning (5204DLFV6Y) realised at the Univeristy of Amsterdam, Fall 2020.
This repository contains the code, data and scripts used to write the Bachelor Thesis "Latent representations for traditional music analysis and generation".
Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow
Solutions for Advanced Image Analysis course assignments, featuring model designs for image summation and generation with MNIST, and style transfer using CycleGAN with MNIST and SVHN datasets.
Encoder and Decoder in VAE's.
Add a description, image, and links to the vae-implementation topic page so that developers can more easily learn about it.
To associate your repository with the vae-implementation topic, visit your repo's landing page and select "manage topics."