Udacity DLND final project . Use GAN generate face
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Updated
May 17, 2017 - HTML
Udacity DLND final project . Use GAN generate face
Generative Adversarial Network (GAN) that generates face images.
Using generative adversarial networks to generate new images of faces (datasets: MNIST, CelebA).
Implementation of DCGAN in Tensorflow and Torch7
The aim of this work is to generate new face images similar to training ones (the CelebA dataset) according to user specified attributes. To do that we ended up with an implementation of a Versatile Auxiliary Classifier + GAN.
A DCGAN is trained on a dataset of faces. A generator network generates new images of faces that look very realistic.
Generate new images of faces using Deep Convolutional Generative Adversarial Networks (DCGANs)
This project is one of the projects required for Deep Learning Nanodegree
Uses generative adversarial networks to create images of faces
A Deep Convolutional Generative Adversarial Neural Networks to generate new images of faces
Tensorflow's 2.0 implementation of StackGANv2
Defined and trained a DCGAN on a dataset of faces. The Goal of this project is to generate new images of faces that look as realistic as possible.
In this project, we'll define and train a DCGAN on a dataset of faces. Our goal is to get a generator network to generate new images of faces that look as realistic as possible!
Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.
Face generation with DCGAN and SNGAN on CelebA dataset
Introduction to Deep Convolutional Generative Adversarial Networks (DCGANs) for Face Generation
Face generation bot utilizing DCGAN structure with TensorFlow.
A project for spouse prediction. Use deep learning and computer vision techniques to generate the appearance of a spouse.
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