Detect image anomalies by using low-dimensional embeddings of patches
Detect image anomalies by using low-dimensional embeddings of patches
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation
arXiv paper abstract https://arxiv.org/abs/2110.15525v1
arXiv PDF paper https://arxiv.org/pdf/2110.15525v1.pdf
A neural network targeting at unsupervised image anomaly localization, called the PEDENet, is proposed
... PEDENet contains a patch embedding (PE) network, a density estimation (DE) network, and an auxiliary network called the location prediction (LP) network.
... PE network takes local image patches as input and performs dimension reduction to get low-dimensional patch embeddings
... DE network takes those patch embeddings and then predicts the cluster membership of an embedded patch.
... LP network ... 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.
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