  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.
