Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
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Updated
Jun 1, 2024 - Python
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.
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
This project demonstrates how to use the transformers library to generate music conditionally using the MusicgenForConditionalGeneration model. The script provides a step-by-step guide to load the model, generate music based on a text description, and handle various issues such as float precision, chunked generation, and silence detection.
A deep generative modeling architecture for designing lattice constrained materials
A machine learning library for detecting anomalies in signals.
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.
A Generative Adversarial Network (GAN) project designed to generate realistic fake handwritten digits, trained on the MNIST dataset.
Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal.
FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids
Official implementation of DrugGEN
HyMPS will be a platform-indipendent software suite for advanced audio/video contents production.
Implementation of the Style GAN Architecture in PyTorch
Benchmarking synthetic data generation methods.
PyTorch implementation (with up-to-date tooling) of the SAM / DAC algorithm
PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
Image Domain Transfer with CycleGAN
Just some notebooks I wrote to research some fun stuff in hobby time
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
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Released June 10, 2014