Synthesized singing voice demos of WeSinger 2 paper.
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Updated
Feb 20, 2023 - HTML
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Synthesized singing voice demos of WeSinger 2 paper.
writing style transfer using cycle gan
We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.
My Projects Submission to Udacity's Deep Learning Nanodegree Program
This repository contains code and bonus content which will be added from time to time for the books "Learning Generative Adversarial Network- GAN" and "R Data Analysis Cookbook - 2nd Edition" by Packt
A PyTorch implementation of "Deep Convolutional Generative Adversarial Networks" with Multiple Discriminator.
A collection of resources and papers on Diffusion Models.
Used GANs to generate new celebrity faces
Cardiac Fats Segmentation Using a Conditional Generative Adversarial Network
Face Generation Models using Deep Convolutional Generative Adversial Networks
Deep Learning Projects
A Deep Learning project to Generate faces using GANs
Generative Adverserial Network for face generation using Tensorflow and DCGAN architecture
Project 5
In this project, you'll use generative adversarial networks to generate new images of faces. 👦 👧 👨 👩 👴 👵
Using generative adversarial networks to generate new images of faces (datasets: MNIST, CelebA).
Generating faces using DCGANs
Using Generative Adversarial Neural Networks to generate new faces
Project 4 for Udacity's Deep Learning Nanodegree. GANs are trained to generate human faces.
Released June 10, 2014