Anomaly Detection via Autoencoder Composite Features and NCE

TL;DR

This paper introduces a novel unsupervised anomaly detection method combining autoencoders with noise contrastive estimation, improving detection accuracy by modeling joint features and optimizing contrastive learning.

Contribution

It proposes a decoupled training approach that integrates autoencoders with NCE to enhance anomaly detection by modeling joint feature spaces and reducing false negatives.

Findings

Matches state-of-the-art performance on benchmark datasets

Effectively reduces false negatives in anomaly detection

Demonstrates robustness across multiple datasets

Abstract

Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution inputs by high reconstruction error or low likelihood, respectively. However, AEs may generalize and achieve small reconstruction errors on abnormal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE). After training the AE, NCE estimates a probability density function, to serve as the anomaly score, on the joint space of the AE's latent representation combined with features of the reconstruction quality. To further reduce the false negative rate in NCE we systematically varying the reconstruction features to augment the training and optimize the contrastive Gaussian noise distribution. Experimental assessments on multiple benchmark datasets demonstrate that the proposed approach matches the performance of prevalent state-of-the-art anomaly detection algorithms.