Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
A machine learning library for detecting anomalies in signals.
Benchmarking synthetic data generation methods.
HyMPS will be a platform-indipendent software suite for advanced audio/video contents production.
This repository contains an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the FashionMNIST dataset. The project aims to generate realistic images of clothing items using a GAN architecture. It includes model definitions, training scripts, and visualizations of generated images at various training stages.
Synthetic data generation for tabular data
State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.
MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications
Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal.
This is part of my dissertation thesis. I'm trying to denoise historical hungarian folk-musical recordings based on Google's article: https://arxiv.org/pdf/2008.02027
[CVPR 2023, top-10%] Authors official PyTorch implementation of the "Attribute-preserving Face Dataset Anonymization via Latent Code Optimization".
Using WGAN-gp and creating art portraits.
Official implementation of DrugGEN
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
Synthetic data generators for tabular and time-series data
This repository contains my solutions to the lab sessions for the course CS F437: Generative AI.
Code for our bachelor's project "Investigating Generative Adversarial Networks for generating synthetic DNA sequences with splice sites" at UGent 2024.
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
A repository dedicated to showcasing and exploring the fascinating world of generative artificial intelligence.
Official Implementation for "HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach"
The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
Released June 10, 2014