Movie Recommender Systems based on Collaborative GAN using movielens ml-100k dataset

MalDataGen is an advanced Python framework for generating and evaluating synthetic tabular datasets using modern generative models, including diffusion and adversarial architectures.

A PyTorch implementation of Conditional GAN (cGAN) to generate specific fashion category images (e.g., sneakers, bags) using FashionMNIST. Features custom Generator/Discriminator architecture with label embeddings, visual validation pipeline, and quantitative evaluation using FID score.

Text-to-Image Generation with Birds using cGAN.

This repository contains the code of exercises included in the book "Hand-on Neural Networks with TensorFlow 2.0" with some extra codes.

Public overview of a deep learning pipeline for CMRO₂ synthesis from multimodal qMRI data using a 3D U-Net conditional GAN.

Three GAN architectures implemented from scratch using PyTorch Lihtning following original research papers.

SyntheticOcean: Open-Source Library for Generating Synthetic Tabular Data + SynDataGen (Framework for Synthetic Data Generation)

This is a Gray-Scale Image Colorizer that is based on a modified Pix2Pix structure

Generating and analyzing synthetic traffic sign images using cGANs and safety CNN

SEWAA Forecasts and Data Ecosystem Wrapper Package

Conditional GAN in PyTorch

Imbalanced data processing for Network Intrusion Detection using Machine Learning. Includes preprocessing, resampling techniques, and classification experiments.

Ce projet a pour but de générer des images synthétiques conditionnées sur des labels non supervisés issus du clustering. puis d’entraîner un générateur conditionné sur ces clusters.

NebulAI is a web app that generates realistic galaxy images using a Conditional GAN (cGAN) guided by K-Means clustering. Users can explore and generate galaxies based on learned visual categories through an interactive React + Django interface.

This is an opensource code of the project I worked on that trains a model to generate Cartoon Faces, using CVAE and cGAN models

🧠 [Signal] Fault-specific vibration signal generation using WGAN-CGAN

Image colorization using pix2pix GAN on landscape images, showcasing conditional GANs for grayscale-to-color translation. A practical example of applying deep learning to image-to-image tasks.

This code implements an example of a CGAN deep learning model using PyTorch. The architecture used for the generator and discriminator is MLP (multi layer perceptron) network. This model is trained with MNIST dataset and finally it can generate images of numbers 0 to 9 according to the label we specify for it.