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 ...

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 ...

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 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 ...

Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many different types of ...

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, ...

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 ...

Learn how autoencoders and GANs can help you with anomaly detection and data compression, and what are their differences and trade-offs. Agree & Join LinkedIn ...

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.