  A neural network targeting at unsupervised image anomaly localization, called
the PEDENet, is proposed in this work. PEDENet contains a patch embedding (PE)
network, a density estimation (DE) network, and an auxiliary network called the
location prediction (LP) network. The PE network takes local image patches as
input and performs dimension reduction to get low-dimensional patch embeddings
via a deep encoder structure. Being inspired by the Gaussian Mixture Model
(GMM), the DE network takes those patch embeddings and then predicts the
cluster membership of an embedded patch. The sum of membership probabilities is
used as a loss term to guide the learning process. The LP network is a
Multi-layer Perception (MLP), which takes embeddings from two neighboring
patches as input and predicts their relative location. The performance of the
proposed PEDENet is evaluated extensively and benchmarked with that of
state-of-the-art methods.
