用自编码器和噪声对比估计联合建模，提升异常检测准确率。
Anomaly Detection via Autoencoder Composite Features and NCE
- 分离训练自编码器与噪声对比估计模型，联合利用隐空间与重构质量特征
- 在多个基准数据集上达到主流先进算法水平的检测性能
- 通过特征增强与噪声分布优化，有效降低漏检率，适合工业异常检测
无监督异常检测是一项挑战性任务。自编码器（AE）或生成模型常用于建模正常输入的数据分布，并通过高重建误差或低似然值识别异常、分布外输入。然而，自编码器可能对异常输入也实现较小的重建误差。本文提出一种解耦训练方法：同时训练一个自编码器和一个基于噪声对比估计（NCE）的概率密度模型。在自编码器训练完成后，NCE 在自编码器隐表示与重构质量特征的联合空间中估计概率密度函数，作为异常得分。为进一步降低NCE中的假阴性率，系统性地变化重构特征以增强训练，并优化对比噪声的高斯分布。在多个基准数据集上的实验表明，该方法性能媲美当前主流先进异常检测算法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。