# DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

arXiv: https://arxiv.org/abs/2303.08730 · HTML: https://mlsanity.com/paper/2303.08730/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 49 of its 62 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 30 of 49)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications](https://mlsanity.com/paper/1701.05517/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Diffusion models as plug-and-play priors](https://mlsanity.com/paper/2206.09012/) (2022)

## Built on by (most cited first, 30 of 46)

- [The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2503.21622/) (2025)
- [ReconDreamer-RL: Enhancing Reinforcement Learning via Diffusion-based Scene Reconstruction](https://mlsanity.com/paper/2508.08170/) (2025)
- [OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning](https://mlsanity.com/paper/2505.22039/) (2025)
- [A Survey on Diffusion Models for Anomaly Detection](https://mlsanity.com/paper/2501.11430/) (2025)
- [PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies](https://mlsanity.com/paper/2506.09237/) (2025)
- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection](https://mlsanity.com/paper/2407.01312/) (2024)
- [When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series](https://mlsanity.com/paper/2506.23596/) (2025)
- [Retrieval Augmented Deep Anomaly Detection for Tabular Data](https://mlsanity.com/paper/2401.17052/) (2024)
- [AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model](https://mlsanity.com/paper/2408.01960/) (2024)
- [Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2510.17611/) (2025)
- [Few-shot Online Anomaly Detection and Segmentation](https://mlsanity.com/paper/2403.18201/) (2024)
- [Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes](https://mlsanity.com/paper/2407.15169/) (2024)
- [Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors](https://mlsanity.com/paper/2510.01934/) (2025)
- [Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2508.00758/) (2025)
- [InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models](https://mlsanity.com/paper/2504.05662/) (2025)
- [Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2504.11055/) (2025)
- [RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2508.02903/) (2025)
- [VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion](https://mlsanity.com/paper/2511.08173/) (2025)
- [MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples](https://mlsanity.com/paper/2511.10047/) (2025)
- [ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects](https://mlsanity.com/paper/2503.04997/) (2025)
- [Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation](https://mlsanity.com/paper/2505.22805/) (2025)
- [Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection](https://mlsanity.com/paper/2508.03539/) (2025)
- [GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection](https://mlsanity.com/paper/2601.01856/) (2026)
- [RAID: Retrieval-Augmented Anomaly Detection](https://mlsanity.com/paper/2602.19611/) (2026)
- [Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models](https://mlsanity.com/paper/2605.23070/) (2026)
- [Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models](https://mlsanity.com/paper/2508.04818/) (2025)
- [ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection](https://mlsanity.com/paper/2510.07927/) (2025)
- [Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion](https://mlsanity.com/paper/2601.08022/) (2026)
- [Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging](https://mlsanity.com/paper/2603.21717/) (2026)

## Abstract (arXiv)

Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, the anomalous region loses its distinctive features after being disturbed by Gaussian noise and is subsequently reconstructed into an anomaly-free one. Afterward, the segmentation sub-network predicts pixel-level anomaly scores based on the similarities and discrepancies between the input image and its anomaly-free reconstruction. Additionally, given the substantial decrease in inference speed due to the iterative denoising nature of diffusion models, we revisit the denoising process and introduce a rapid one-step denoising paradigm. This paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality. Furthermore, considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches and achieves comparable inference speed, demonstrating the effectiveness and broad applicability of the proposed pipeline. Code is released at https://github.com/HuiZhang0812/DiffusionAD