An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection
Shuo Liu, Liwen Xu · IEEE Access · 2020
Anomaly detection is a classic and crucial problem in the field of artificial intelligence, which aims to find instances that deviate so much from the main distribution of data or differ from known instances. This paper explores how to combine advanced deep learning techniques with traditional probabilistic statistical methods for anomaly detection. We propose a very effective and concise semi-supervised anomaly detection method named “ORGAN-KDE” based on the orthogonal generative adversarial network (O-GAN) and kernel density estimation. In the training phase, we use the encoder of O-GAN to learn the latent representation of normal data, namely the code of normal data, and then use the kernel density estimation to solve the probability density function of code. The code of normal sample obtained through the trained encoder can get a larger probability value when passing through the trained kernel density estimator, while the code of anomalous sample can get a smaller probability value, so as to achieve the purpose of anomaly detection. Compared with other anomaly detection methods based on GAN, our method has a very simple network structure, and experiments have proved that it performs well on both structured datasets and image datasets.