Anomaly detection plays a pivotal role in numerous real-world scenarios, such as industrial automation and manufacturing intelligence. Recently, variational inference-based anomaly analysis has attracted researchers' and developers' attention. It aims to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need us to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability; (ii) the strong probability distance notion results in collapsed features. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness of the latent manifold. This makes using the model on high-dimensional practical data possible. In addition, we leverage a weaker measure, sliced-Wasserstein distance, to achieve the equilibrium between the reconstruction fidelity and generalized representations. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method.

翻译：异常检测在许多现实情景中发挥着关键作用,例如工业自动化和制造情报。最近,基于不同推断的异常分析吸引了研究人员和开发者的注意。它旨在模拟无缺陷分布模式,以便将异常分类为分配之外的样本。然而,有两个令人不安的因素需要我们优先处理:(一) 简单化的先前潜在分布导致有限的表达能力;(二) 强烈的概率距离概念在崩溃的特征中产生结果。在本文件中,我们提出了一个新的Patch-Wasserstein Auto Encoder(P-WAE)结构,以缓解这些挑战。特别是,设计了一个与解决jigsaw拼图相配合的不完全的互通性推论模型,这是提高潜在外层的清晰度的一个简单而有效的方法。这使我们能够利用高维实用数据的模型。此外,我们利用了一个较弱的尺度,即切片-Wasserstein距离,以达到重建对等和普遍表述之间的平衡。在MVTEDADD数据集上进行了全面实验,展示了我们拟议方法的优异性表现。