Generate high quality pictures. GAN. Generative Adversarial Networks

Last update: Dec 14, 2021

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Overview

ESRGAN

generate high quality pictures. GAN. Generative Adversarial Networks """ Super-resolution of CelebA using Generative Adversarial Networks. The dataset can be downloaded from: https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AADIKlz8PR9zr6Y20qbkunrba/Img/img_align_celeba.zip?dl=0 (if not available there see if options are listed at http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) Instrustion on running the script:

Download the dataset from the provided link
Save the folder 'img_align_celeba' to '../../data/'
Run the sript using command 'python3 esrgan.py' """

Requirements

torch>=0.4.0 
torchvision 
matplotlib 
numpy
scipy
pillow
urllib3
scikit-image

Owner

Lieon

Deep learning, Anomaly detection,Time series, Generative Adversarial Networks, Adversarial attack.

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Generate high quality pictures. GAN. Generative Adversarial Networks

Related tags

Overview

ESRGAN

Requirements

Owner

Lieon

Style-based Neural Drum Synthesis with GAN inversion

Full Stack Deep Learning Labs

This repository contains part of the code used to make the images visible in the article "How does an AI Imagine the Universe?" published on Towards Data Science.

This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers."

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