Generate fake faces using generative adversarial network

The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.

Implementation of the paper "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data." 🖼️

A pytorch implementation of GAN

My own implementation of a GAN in the BEGAN architecture with pytorch

Image Generative Adversarial Networks (GANs) have revolutionized the field of computer vision by enabling the generation of realistic and high-quality images. GANs consist of two neural networks: a generator network that synthesizes images, and a discriminator network that tries to distinguish real images from the generated ones.

Investigate mapping of articulations from the image space to the latent space using neural networks.

Adversarial Attack using a DCGAN

Create images of Pokemon using a Deep Convolutional Generative Adversarial Network.

A python package that'll help you train DCGAN models with your own image based data.

DCGAN implementation

discord python cog for discord tag finder , discrim finder , discrim lookup , tag lookup , 0001 lookup

Pytorch implementation of official DCGAN.

Implementation of some types of GANs (Deep convolutional GAN - Wasserstein GAN - conditional GAN) with PyTorch library

An unofficial pytorch implementation for OGAN

Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.

The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.