(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare

StyleGAN and StyleGAN2 implementation for generating anime faces.

This system provides the facility to convert roughly hand-drawn humam face sketch image on canvas into a realistic face image by using image generative AI in a real-time.

A cartoonizer able to turn your images or videos to high-quality real-life cartoons using the power of GANs and Vgg16 NN archticture

The first GANs-based omics-to-omics translation framework

Implementing Age Transformation using Generative Models.

StyleGAN2-ADA for generation of synthetic skin lesions

A curated set of Jupyter notebooks showcasing various PyTorch projects and tutorials, covering deep learning fundamentals, computer vision, GANs, transfer learning, transformers and more. Continuously updated with new experiments, models and practical implementations for learning and research.

This repository uses modules from Swin Transformer to build Transformer-based Generative Adversarial Networks (GANs) models.

Generative Adversarial Networks for financial time series

Open source code and ressources for SIP-GAN paper.

A MNIST GAN implemented from scratch in C++

Cropped Image Enhancement using GANs for YOLO

An AI/ML model that uses Generative Adversarial Networks to create images from a sample dataset. 5th Attempt

This repository will contain the codes and information from my Image Colorization project.

Integrating LSTM with GAN's Model

Generating different facial expressions.It uses diferent types of Generative Adversarial Network to generate the required outputs.

This project investigates the application of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) for generating synthetic urban layouts using Open Street Map (OSM) tile datasets from Gurugram, India, and California, USA.

Code and results for a paper on early melanoma skin cancer detection. Implements and compares WGAN, RaLSGAN, and CycleGAN to correct class imbalance in dermatological image datasets, with a VGG16-based classifier used to evaluate downstream detection performance on the balanced data.

Small, fast and intuitively clear Generative Adversarial Network.