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GitHub10d
A PyTorch implementation of a Variational Autoencoder trained on the MNIST dataset. The project demonstrates digit generation from random latent vectors and reconstruction of ...
This project implements a Variational Autoencoder (VAE) in PyTorch, trained on the MNIST dataset. It features a Gradio web app for generating new digits and reconstructing uploaded digit images ... or ...
GitHub4d
Pytorch implementation for image compression and reconstruction via autoencoder
This is an autoencoder with cylic loss and coding parsing loss for image compression and reconstruction. Network backbone is simple 3-layer fully conv (encoder) and symmetrical for decoder. Finally it ...
IEEE4d
Plug-And-Play Image Reconstruction Meets Stochastic Variance-Reduced Gradient Methods
Abstract: Plug-and-play (PnP) methods have recently emerged as a powerful framework for image reconstruction that can flexibly combine different physics-based observation models with data-driven image ...
IEEE2d
Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow—Editorial for the 2nd Special Issue “Machine Learning for Image Reconstruction”
Abstract: As a follow-up to the first IEEE Transactions on Medical Imaging (TMI) special issue on the theme of deep tomographic reconstruction, the second special issue is assembled to reflect the ...