Anomaly Detection Papers Guide

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)

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']}")

# PatchCore-style anomaly detection
from anomalib.data import MVTec
from anomalib.models import Patchcore
from anomalib.engine import Engine

# Setup dataset
datamodule = MVTec(
    root="./datasets/MVTec",
    category="bottle",
    image_size=(256, 256),
)

# Initialize model
model = Patchcore(
    backbone="wide_resnet50_2",
    layers=["layer2", "layer3"],
    coreset_sampling_ratio=0.1,
)

# Train and test
engine = Engine()
engine.fit(model=model, datamodule=datamodule)
results = engine.test(model=model, datamodule=datamodule)
print(f"Image AUROC: {results[0]['image_AUROC']:.3f}")
print(f"Pixel AUROC: {results[0]['pixel_AUROC']:.3f}")

# Standard anomaly detection metrics
from sklearn.metrics import roc_auc_score
import numpy as np

# Image-level: Is this image anomalous?
image_auroc = roc_auc_score(y_true_image, y_score_image)

# Pixel-level: Where is the anomaly?
pixel_auroc = roc_auc_score(
    y_true_pixel.flatten(), y_score_pixel.flatten()
)

# PRO metric: Per-Region Overlap
# Better than pixel AUROC for small anomalies
# Weights each connected anomaly region equally

### Active Directions (2024-2025)
1. **Zero/few-shot AD** — Detect anomalies without normal training data
2. **Multi-class unified** — One model for all product categories
3. **Foundation model AD** — CLIP/SAM/LLM-based detection
4. **Logical anomalies** — Structural/contextual defects
5. **Continual learning** — Adapt to new defect types
6. **3D anomaly detection** — Point cloud and multi-modal
7. **Real-time deployment** — Edge device optimization