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!

Vanilla GAN implementation with PyTorch

These tutorials are for beginners who need to understand deep generative models.

Simulate experiments with the Vanilla GAN architecture and training algorithm in PyTorch using this package.

Image generation using Vanilla GAN (General Adversarial Network)

TensorFlow Generative Adversarial Networks (GANs)

Implementations of different Generative Adversarial Networks

Synthetic Data Generation (SDG) Using Vanilla GAN

Speech-Recognition STT Project

Standard Deep Learning Models implemented in pytorch framework

Vanilla GAN implementation on MNIST dataset using PyTorch

Implement multiple gan including vanilla_gan, dcgan, cgan, infogan and wgan with tensorflow and dataset including mnist.

PyTorch implementation of Vanilla GAN

Generative Adversarial Networks in TensorFlow 2.0

My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN (Goodfellow et al.), cGAN (Mirza et al.), DCGAN (Radford et al.), etc.

Simple Implementation of many GAN models with PyTorch.