That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

Additionally, utilizing stacked autoencoder architecture with layer-wise pre-training and hierarchical structures, GMScaleSAE effectively fuses multiscale features, ensuring stable training and ...

In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates ...

This repository contains the implementation of a Triplet Variational Autoencoder (Tri-VAE) designed to detect anomalies in brain MRI scans. The method is inspired by the CVPR 2024 paper: "Triplet ...

However, relatively little research has addressed open-set learning issues involving unknown working modes. A multifunction radar working mode open-set recognition method based on dual autoencoder ...

This project implements an end-to-end solution for automatic image colorization using a U-Net autoencoder. The model takes grayscale images as input and generates realistic colorized versions while ...