Radiation Anomaly Detection Using an Adversarial Autoencoder Abstract: Scintillators are the primary devices used for radiation detection., especially at national borders and other ports of entry.

This repository provides an implementation of an anomaly detection system for cell images using autoencoders. The project draws inspiration from the paper "Robust Anomaly Detection in Images using ...

Using Dropout In situations where a neural model tends to overfit, you can use a technique called dropout. For an autoencoder anomaly detection system, model overfitting is characterized by a ...

This repository contains a Jupyter Notebook that demonstrates how to perform anomaly detection in time series data using an Autoencoder neural network. The notebook explores a deep learning approach ...

16. Schreyer M, Sattarov T, Schulze C, Bernd R, Damian B. Detection of accounting anomalies in the latent space using adversarial autoencoder neural networks, 2nd KDD workshop on anomaly detection in ...

Data Anomaly Detection Using a Neural Autoencoder with C#. 04/15/2024; Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, ...

Scintillators are the primary devices used for radiation detection., especially at national borders and other ports of entry. Reducing the size of these detectors for placement on mobile devices such ...