NumPy Implementation of a Dense Denoising Autoencoder for the MNIST Dataset. With this project I wanted to implement a simple denoising autoencoder from the bottom up using only NumPy. This project is ...

At 1 sample per pixel (spp), the Monte Carlo integration of indirect illumination results in very noisy images, and the problem can therefore be framed as reconstruction instead of denoising. Previous ...

Este projeto implementa um Autoencoder Denoising utilizando a biblioteca Keras para remover ruído de imagens de dígitos manuscritos. O modelo é treinado no dataset MNIST, onde imagens originais são ...

Imputation of incomplete medical data using missing neighborhood perturbation denoising autoencoder Abstract: The problem of missing data in the clinical environment is a common one. However, the ...

Keywords: protein–protein interactions, cardiovascular disease, deep denoising autoencoder, CatBoost, evolutionary information. Citation: Zhou S, Luo J, Tang M, Li C, Li Y and He W (2025) Predicting ...

Abstract: This paper presents a method for detecting abnormal load in distributed power stations using a denoising autoencoder algorithm optimized by a biased random key genetic algorithm. Our method ...

Ideally, any suitable denoising autoencoder can be used, but we adopt the U-Net architecture (Ronneberger et al., 2015) originally developed for medical imaging applications and is robust for various ...