論文紹介 Anomaly Detection using One-Class Neural Networks (修正版

This document discusses anomaly detection using one-class neural networks (OC-NN). It begins by introducing one-class support vector machines (OC-SVM) which learn a decision boundary to distinguish normal data points from anomalies using only normal data for training. The document then presents OC-NN as an alternative, where a neural network is trained to learn a low-dimensional representation of only normal data, and anomalies are detected as points with a large reconstruction error. It evaluates OC-NN on several datasets, finding it can achieve good performance compared to OC-SVM at detecting anomalies, as measured by the area under the ROC curve metric.