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anomaly-detection-papers-guide

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Industrial anomaly detection methods and benchmark papers

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# Industrial Anomaly Detection Papers Guide

## Overview

Industrial anomaly detection uses machine learning to identify defects, faults, and anomalies in manufacturing and quality inspection. This curated collection covers methods from reconstruction-based (autoencoders) to memory-bank approaches (PatchCore), normalizing flows, knowledge distillation, and foundation model-based detectors. Includes benchmark datasets, evaluation metrics, and real-world deployment considerations.

## Method Taxonomy

```
Anomaly Detection Methods
├── Reconstruction-based
│   ├── Autoencoder (AE, VAE)
│   ├── GAN-based (AnoGAN, GANomaly)
│   └── Diffusion-based (AnoDDPM)
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