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 ...

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 ...

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

These altered inputs create a security risk in applications with real-world consequences, such as self-driving cars, robotics and financial services. We propose an unsupervised method for detecting ...

The code for TadGAN is open-source and now available for benchmarking time series datasets for anomaly detection. The paper, titled “TadGAN: Time Series Anomaly Detection Using Generative Adversarial ...