Deep Structured Energy Based Models for Anomaly Detection

Understand

In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures.

• We propose deep structured energy based models (DSEBMs), where the energy function is the output of a deterministic deep neural network with structure.
• We develop novel model architectures to integrate EBMs with different types of data such as static data, sequential data, and spatial data, and apply appropriate model architectures to adapt to the data structure.
• Our training algorithm is built upon the recent development of score matching \cite{sm}, which connects an EBM with a regularized autoencoder, eliminating the need for complicated sampling method.