Utility files for Granular challenge.
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Updated
Apr 14, 2017 - 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.
Utility files for Granular challenge.
This repository houses projects completed as part of the Google Career Certificates program offered through the Orange Learning initiative, sponsored by Orange Digital Center, Google, and Coursera.
Use of Generative Adversarial Networks (GAN's) to generate new images of faces.
This is Udacity - Deep Learning Project 4 - Generate Faces
Taller de ML (Aprendizaje de Máquina) para crear imágenes artísticas (Generative Art) con redes Adversarias Generadoras y Condicionadas (GAN/CGAN) con los datos MINST de moda (Fashion MINST).
Face generation project for the DLND, Project 5
Projects for Udacity Deep Learning Nanodegree Program: CNN, RNN, GANs
Generative Adverserial Network to create faces from CelebA dataset (P4 - DLND)
A generative adversarial network (GAN) that generates images of faces.
Experiments with Baudelaire and a text-to-image GAN.
GAN that can generate face images.
Udacity DLND final project . Use GAN generate face
I have used Deep Convolutional Generative Adversarial Networks for generating faces and mnist images
Repository for Slide Deck and Code Examples for talk at SDP Convening 2023
Its a image generation library which learns to generate patterns based on training data
A framework to synthsize Brain data using AI models
Generate images to handle imbalanced datasets using DCGAN
Released June 10, 2014