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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
Implementing various GAN Architectures in PyTorch.
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Oct 18, 2020
Jupyter Notebook
WGAN with feedback from discriminator& LayerNorm instead of BatchNorm
Updated
Sep 12, 2020
Python
Deep Convolutional Generative Adversarial Network (DCGAN) for generating gemstones.
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Nov 6, 2019
Jupyter Notebook
Updated
Apr 14, 2021
Python
TensorFlow Generative Adversarial Networks (GANs)
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Jul 24, 2019
Python
Master's Final Project: Adversarial Domain Adaptation Super Resolution
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Sep 19, 2019
Python
Project to get attention from discriminator: 1st combination
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Sep 12, 2020
Python
PyTorch implementations of Generative Adversarial Networks (GAN) variants: Vanilla GAN, Nano GAN, WGAN, WGAN-GP, StarGAN
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Aug 3, 2024
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
A walkthrough of two GAN implementations (DCGAN and WGAN_GP)
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Feb 22, 2018
Python
This is a deep learning code written in PyTorch that convert a given text into image.
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May 3, 2018
Python
[BETA] Easy-to-use pytorch model trainer, dataloader creators, and utilities + my own implementations of select models
Updated
Jan 27, 2023
Python
Updated
Aug 28, 2022
Python
DCGAN implementation in PyTorch to generate company / corporate logos, based on the Large Logo Dataset, and seed phrases.
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Feb 10, 2022
Python
3 experiments of GAN/wGAN on simple gaussian distribution, MNIST dataset and exploration of music generation by MuseGAN.
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Feb 23, 2021
Jupyter Notebook
Updated
Oct 27, 2022
Python
DWGAN&WGAN for cifar10 (only car images)
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Aug 9, 2020
Python
Updated
Oct 29, 2020
Jupyter Notebook
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