Anomaly Detection via Autoencoder Composite Features and NCE

2025-02-04

AI Summary

In unsupervised anomaly detection, autoencoders (AEs) often suffer from over-generalization to anomalous samples, resulting in underestimated reconstruction errors and high false-negative rates. To address this, we propose a decoupled joint-training framework that disentangles and fuses AE latent-space representations with reconstruction-quality features, yielding more discriminative composite features. Furthermore, we introduce an optimizable contrastive Gaussian noise distribution to enhance the robustness of noise-contrastive estimation (NCE)-based density modeling. Evaluated on multiple standard benchmarks, our method achieves state-of-the-art performance among AE-based approaches—significantly reducing false-negative rates while maintaining low false-positive rates. This work establishes a novel paradigm for improving the reliability of AE-based models in anomaly detection.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Unsupervised anomaly detection challenge
• Autoencoder generalization issue on anomalies
• Decoupled training with NCE for accuracy

Innovation

• Autoencoder composite features
• Noise contrastive estimation
• Contrastive Gaussian noise optimization