arXiv 2403.133492024-07-04cs.LGcs.CV

面向统一异常检测的分层高斯混合归一化流建模

Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Intelligent Manufacturing, Wuxi Vocational College of Science and Technology（无锡科技职业学院智能制造学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院、教育部人工智能重点实验室）

Xincheng Yao, Ruoqi Li, Zefeng Qian, Lu Wang, Chongyang Zhang

备注：: This paper is accepted by ECCV2024

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AI总结：

本文提出HGAD方法，通过类间高斯混合建模和类内混合类中心学习，结合互信息最大化损失，缓解归一化流在统一异常检测中的同质映射问题，并在四个基准上取得优于现有方法的性能。

摘要：

统一异常检测（AD）是异常检测中最具挑战性的任务之一，其目标是用来自多个类别的正常样本训练一个统一模型，以检测这些类别中的异常。对于这一具有挑战性的任务，流行的基于归一化流（NF）的异常检测方法可能会陷入“同质映射”问题，即基于NF的异常检测模型倾向于为正常和异常特征生成相似的潜在表示，从而导致较高的异常漏检率。在本文中，我们提出了一种新颖的分层高斯混合归一化流建模方法，用于实现统一异常检测，我们称之为HGAD。我们的HGAD包含两个关键组成部分：类间高斯混合建模和类内混合类中心学习。与之前的基于NF的异常检测方法相比，分层高斯混合建模方法能够为归一化流的潜在空间带来更强的表示能力，使得即使是复杂的多类别分布也能在潜在空间中得到很好的表示和学习。通过这种方式，我们可以避免将不同的类别分布映射到同一个单一高斯先验中，从而有效避免或缓解“同质映射”问题。我们进一步指出，不同类中心之间的可区分性越强，越有利于避免偏差问题。因此，我们进一步提出了一种互信息最大化损失，以更好地构建潜在特征空间。我们在四个真实世界的异常检测基准上评估了我们的方法，在这些基准上，我们能够显著改进之前的基于NF的异常检测方法，并且优于最先进的统一异常检测方法。

英文摘要：

Unified anomaly detection (AD) is one of the most challenges for anomaly detection, where one unified model is trained with normal samples from multiple classes with the objective to detect anomalies in these classes. For such a challenging task, popular normalizing flow (NF) based AD methods may fall into a "homogeneous mapping" issue,where the NF-based AD models are biased to generate similar latent representations for both normal and abnormal features, and thereby lead to a high missing rate of anomalies. In this paper, we propose a novel Hierarchical Gaussian mixture normalizing flow modeling method for accomplishing unified Anomaly Detection, which we call HGAD. Our HGAD consists of two key components: inter-class Gaussian mixture modeling and intra-class mixed class centers learning. Compared to the previous NF-based AD methods, the hierarchical Gaussian mixture modeling approach can bring stronger representation capability to the latent space of normalizing flows, so that even complex multi-class distribution can be well represented and learned in the latent space. In this way, we can avoid mapping different class distributions into the same single Gaussian prior, thus effectively avoiding or mitigating the "homogeneous mapping" issue. We further indicate that the more distinguishable different class centers, the more conducive to avoiding the bias issue. Thus, we further propose a mutual information maximization loss for better structuring the latent feature space. We evaluate our method on four real-world AD benchmarks, where we can significantly improve the previous NF-based AD methods and also outperform the SOTA unified AD methods.

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