Therefore, anomaly detection methods are investigated. In this work we employ adversarial autoencoders trained to classify spectra from radioactive sources as either background or anomalous. This ...

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 ...

The Overall Program Structure The overall structure of the PyTorch autoencoder anomaly detection demo program, with a few minor edits to save space, is shown in Listing 3. I prefer to indent my Python ...

The project focuses on detecting anomalies in images using autoencoder neural networks ... thereby degrading anomaly detection performance. To mitigate this issue, an adversarial autoencoder ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional ... Our method was compared with 13 popular outlier detection methods using ...

The autoencoder provides an anomaly detection algorithm for radiation treatment planning. It can detect a very small percentage of abnormal plans in a large number of radiotherapy plans with high ...

Therefore, the autoencoder input and output both have 65 values -- 64 pixel grayscale values (0 to 16) plus a label (0 to 9). Notice that the demo program analyzes both the predictors (pixel values) ...