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)
├── Embedding-based
│   ├── Memory bank (PatchCore, PaDiM)
│   ├── Knowledge distillation (STPM, RD4AD)
│   └── Self-supervised (CutPaste, DRAEM)
├── Normalizing Flows
│   ├── FastFlow, CFLOW-AD, CS-Flow
│   └── DifferNet
├── Foundation Models
│   ├── CLIP-based (WinCLIP, AnomalyCLIP)
│   ├── SAM-based (GroundedSAM-AD)
│   └── Vision-language (AnomalyGPT)
└── 3D Anomaly Detection
    ├── Point cloud methods
    └── Multi-modal (RGB + 3D)

Key Methods

Method	Year	Approach	MVTec AUROC
PatchCore	2022	Memory bank	99.1%
PaDiM	2021	Multivariate Gaussian	97.9%
RD4AD	2022	Knowledge distillation	98.5%
FastFlow	2022	Normalizing flow	99.4%
SimpleNet	2023	Feature adaptation	99.6%
WinCLIP	2023	CLIP zero-shot	95.2%
AnomalyGPT