基于自编码器复合特征与NCE的异常检测
Anomaly Detection via Autoencoder Composite Features and NCE
AI总结：
针对自编码器易对异常输入产生低重构误差的问题，提出结合自编码器复合特征与NCE的解耦异常检测方法，通过特征扩充和噪声分布优化降低假阴性率，性能达主流SOTA水平。
摘要：
无监督异常检测是一项具有挑战性的任务。研究人员通常采用自编码器（Autoencoders, AEs）或生成模型对正常输入的数据分布建模，随后分别通过高重构误差或低似然值识别异常的分布外输入。然而，自编码器可能具备泛化能力，在异常输入上也能得到较小的重构误差。我们提出一种用于异常检测的解耦训练方法，该方法同时训练一个自编码器和一个采用噪声对比估计（Noise Contrastive Estimation, NCE）训练的似然模型。在训练完自编码器后，NCE会在自编码器的潜在表示与重构质量特征相结合的联合空间上估计概率密度函数，以此作为异常分数。为进一步降低NCE中的假阴性率，我们系统地改变重构特征以扩充训练数据，并优化对比高斯噪声分布。在多个基准数据集上的实验评估表明，所提方法的性能与当前主流的先进异常检测算法相当。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
