GitHub

Deep Autoencoders model for Anomaly Detection

"Objective: This project focuses on the application of Autoencoders in Deep Learning, particularly for learning compressed representations of data. Autoencoders consist of two main components: an ...
GitHub

Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals

There was an error while loading. Please reload this page. This repository contains the implementation of a variational autoencoder (VAE) for generating synthetic EEG ...
Frontiers

Loop closure detection of visual SLAM based on variational autoencoder

1 Department of College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China 2 Yantai Tulan Electronic Technology Co., Ltd, Yantai, China Loop ...
IEEE

Improved Autoencoder Model With Memory Module for Anomaly Detection

Abstract: As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In ...
IEEE

Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection

Abstract: Image reconstruction-based methods with autoencoder have been widely used for unsupervised anomaly detection. By training the reconstruction on normal ...
Frontiers

Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response

Anticancer drug responses can be varied for individual patients. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to ...