GitHub

Samgeorge2003/Conditional-Variational-Autoencoder-CVAE

Objective: To implement a CVAE, train it on a dataset of your choice (e.g., MNIST, Fashion MNIST, or a dataset of images with associated attributes), and generate new data points conditioned on ...
GitHub

ti2ni1m/Image-Generation-with-Conditional-Variational-Autoencoder-C-VAE-and-Conditional-GAN-C-GAN-

In this project, I aim to create a set of images of Kuzushiji Japanese characters via the Kuzushiji-49 dataset using 2 graphical models which are Conditional-Variational Autoencoder(C-VAE), and ...
IEEE

Conditional Generative Flow-Induced Variational Autoencoder for Plasma Instances Augmentation

Abstract: In this article, we propose a novel conditional generative flow-induced variational autoencoder (CGlow-VAE) model to address the critical challenge of the small sample issue in plasma ...
University of Waterloo

Generative deep learning for probabilistic streamflow forecasting: conditional variational auto-encoder

Generative deep learning for probabilistic streamflow forecasting: conditional variational auto-encoder Citation: Jahangir, M. S. , & Quilty, J. . (2024). Generative deep learning for probabilistic ...
Frontiers

Deep conditional generative model for personalization of 12-lead electrocardiograms and cardiovascular risk prediction

Background: 12-lead electrocardiograms (ECGs) are a cornerstone for diagnosing and monitoring cardiovascular diseases (CVDs). They play a key role in detecting abnormalities such as arrhythmias and ...