A project on generating faces using Deep Convolutional Generative Adversarial Networks which is trained on celebrity faces
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Updated
Oct 30, 2020 - Jupyter Notebook
A project on generating faces using Deep Convolutional Generative Adversarial Networks which is trained on celebrity faces
Random Anime Face Generation with Generative Adversarial Networks. DCGAN and WGAN-GP are used.
A web-app based on Wasserstein Generative Adversarial Network architecture with GP that generates multiple realistic paintings, trained on 8k Albrecht Dürer's paintings, includes super-res mode.
Training a DCGAN to generate new images of faces that look realistic as possible.
DCGAN Fashion MNIST generator
The implementation of DCGAN using PyTorch to generate adversarial examples of pokemon.
Generating Faces using DCGANs as a part of Udacity's Deep learning Nanodegree
Deep Convolutional GAN trained to generate anime faces.
Deep Convolutional GANs to generate new Pokemon
Generate Lego Minifigures & Faces implementing four different type of GANs in Pytorch
Simple DCGAN in python for image generation. Proof-of-concept.
Road towards diffusion models.
In this project, we will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits.
To generate fake faces as accurate as possible with simplified CelebA data set using Discriminator Network
Repository for projects developed in Udacity's Deep Learning Nanodegree.
PyTorch implementation of various GAN architectures.
This repository Investigates DCGAN using facedata. Serves as a personal cautionary tale when working with GANS.
PyTorch implementations of simple GANs for lightweight generative modeling of input distributions.
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