# PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation

arXiv: https://arxiv.org/abs/2110.15525 · HTML: https://mlsanity.com/paper/2110.15525/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](https://mlsanity.com/paper/2008.12577/) (2020)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection](https://mlsanity.com/paper/2005.14140/) (2020)
- [Towards Visually Explaining Variational Autoencoders](https://mlsanity.com/paper/1911.07389/) (2019)
- [Puzzle-AE: Novelty Detection in Images through Solving Puzzles](https://mlsanity.com/paper/2008.12959/) (2020)
- [AnomalyHop: An SSL-based Image Anomaly Localization Method](https://mlsanity.com/paper/2105.03797/) (2021)
- [Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations](https://mlsanity.com/paper/2104.05940/) (2021)

## Built on by (most cited first, 11 of 11)

- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2312.07495/) (2023)
- [Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection](https://mlsanity.com/paper/2403.13349/) (2024)
- [Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval](https://mlsanity.com/paper/2308.06748/) (2023)
- [Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark](https://mlsanity.com/paper/2404.10760/) (2024)
- [UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection](https://mlsanity.com/paper/2307.12540/) (2023)
- [Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers](https://mlsanity.com/paper/2306.03492/) (2023)
- [Self-Supervised Training with Autoencoders for Visual Anomaly Detection](https://mlsanity.com/paper/2206.11723/) (2022)
- [Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development](https://mlsanity.com/paper/2409.06367/) (2024)
- [Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization](https://mlsanity.com/paper/2407.03130/) (2024)
- [Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization](https://mlsanity.com/paper/2509.05034/) (2025)

## Abstract (arXiv)

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.