The Data Science Lab Data Anomaly Detection Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that ...

It is trained to recognize the normal detector behavior from existing good data and to detect any deviations. The cornerstone of this approach is an autoencoder-based anomaly detection system.

SISLAB Junior chose to design a variational autoencoder that detects anomalies on the surface of objects, and for the contest specifically, the surface of a chestnut. Professor Tran Xuan Tu, the ...

To use an autoencoder for anomaly detection, you compare the reconstructed version of an image with its source input. If the reconstructed version of an image differs greatly from its input, the image ...

Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years.

In this article, the authors discuss how to detect fraud in credit card transactions, using Random Forest, Logistic Regression, Isolation Forest and Neural Autoencoder.