anomaly-detection-papers-guide

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	2024	Vision-language	96.3%

Benchmark Datasets

benchmarks = {
    "MVTec AD": {
        "categories": 15,
        "images": 5354,
        "type": "Product/texture defects",
        "annotation": "Pixel-level masks",
    },
    "MVTec 3D-AD": {
        "categories": 10,
        "images": 4147,
        "type": "3D point cloud + RGB",
    },
    "VisA": {
        "categories": 12,
        "images": 10821,
        "type": "Complex structure anomalies",
    },
    "BTAD": {
        "categories": 3,
        "images": 2830,
        "type": "Industrial body/surface",
    },
    "MPDD": {
        "categories": 6,
        "images": 1064,
        "type": "Metal parts defects",
    },
}

for name, info in benchmarks.items():
    print(f"{name}: {info['categories']} categories, "
          f"{info['images']} images — {info['type']}")