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This repository encompasses a comprehensive research of Generative Adversarial Networks (GANs) for Biomaterial Discovery. Our research delves into the generation of intricate biomaterial topographies through the innovative application of AI/ML techniques. Discover our findings, code implementations and datasets in this repository!
Updated
Jul 7, 2024
Jupyter Notebook
Improving Deep Learning accuracy using Generative Adversarial Networks (GANs). Implemented in PyTorch
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Dec 12, 2020
Python
Implementing various GAN Architectures in PyTorch.
Updated
Oct 18, 2020
Jupyter Notebook
Project to get attention from discriminator: 1st combination
Updated
Sep 12, 2020
Python
Updated
Mar 1, 2024
Shell
Block-by-block implementation from scratch of CNNs and GANs using PyTorch, with language models to be added later.
Updated
Aug 9, 2024
Python
Updated
Oct 27, 2022
Python
A walkthrough of two GAN implementations (DCGAN and WGAN_GP)
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Feb 22, 2018
Python
Updated
Aug 28, 2022
Python
Updated
Apr 19, 2018
Python
Code to reproduce experiments in "Convergence Properties of Generative Adversarial Networks".
Updated
Apr 16, 2021
Python
Some demo GAN models implemented with pytorch, including GAN, WGAN, WGAN-GP, CGAN.
Updated
Jul 25, 2022
Python
This repository contains some code for demonstrating the application of Wasserstein GANs (WGANs)
Updated
Jul 30, 2023
Python
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
Updated
May 23, 2017
Python
Updated
Dec 19, 2018
Jupyter Notebook
[Python3 Version] Code for reproducing experiments in "Improved Training of Wasserstein GANs"
Updated
Jun 28, 2018
Python
GAN and WGAN experiments on MNIST and CelebA (Written for ECE6254 Final Project)
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Apr 21, 2020
Python
GANs 101 and its Applications: An exploration of DCGAN, WGAN, LSGAN and GAN transfer learning on the CelebA dataset
Updated
Apr 10, 2023
Jupyter Notebook
This project trains a WGAN on the MNIST dataset to generate images of handwritten digits.
Updated
Jan 10, 2024
Jupyter Notebook
Updated
Mar 4, 2018
Python
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