Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper

DCGAN model to generate metal surface defect images

The objective of this project is to use the following Kaggle dataset (https://www.kaggle.com/datasets/soumikrakshit/anime-faces), in order to generate anime faces using a Deep Convolutional Generative Adversarial Network (DCGAN).

Simple implement of DCGAN on CIFAR10 with one code

Repo for Implementing Research Papers & Projects related to Machine Learning

Generation Of Synthetic Images From Fashion MNIST Dataset With DCGANs In Keras.

Generating simpson faces using Deep Convolutional Generative Adversarial Networks, written in PyTorch.

Road towards diffusion models.

Repository of all notebooks used in the GANs and VAEs event.

Implementations of GANs

Generate Lego Minifigures & Faces implementing four different type of GANs in Pytorch

This is repository teaching PyTorch1.0.

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.

Deep Convolutional Generative Adversarial Networks with Spectral Normalization

PyTorch implementation of various GAN architectures.

Fake Face Generation using DCGANs

DCGAN implementation using PyTorch