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.

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

Simple VAE face generator

A simple implementation of variational Auto encoders using Mnist dataset in tensorflow.

Running VAEs on mobile and IOT devices using TFLite.

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".

Basic implementation of VAE

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.

Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow

Classical Machine Learning Algorithms

Utilized a VAE (Variational Autoencoder) and CGAN (Conditional Generative Adversarial Network) models to generate synthetic chatter signals, addressing the challenge of imbalanced data in turning operations. Compared othe performance of synthetic chatter signals.

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.

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.

Convolutional Variational Autoencoder on VizdoomTakeCover

A variational autoencoder can be defined as being an autoencoder whose training is regularised to avoid overfitting and ensure that the latent space has good properties that enable generative process.

Handwritten Digit Generation with VAE and GAN are applied.