# TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection

arXiv: https://arxiv.org/abs/2311.09999 · HTML: https://mlsanity.com/paper/2311.09999/

## Key facts

- Cited by 43 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 32 of its 45 references that are in the map.
- First posted on arXiv 16 Nov 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 32)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [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)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [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)
- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
- [Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise](https://mlsanity.com/paper/2208.09392/) (2022)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [SimpleNet: A Simple Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2303.15140/) (2023)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection](https://mlsanity.com/paper/2211.11317/) (2022)
- [FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis](https://mlsanity.com/paper/2204.09934/) (2022)
- [Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection](https://mlsanity.com/paper/2110.02855/) (2021)
- [Asymmetric Student-Teacher Networks for Industrial Anomaly Detection](https://mlsanity.com/paper/2210.07829/) (2022)
- [DSR -- A dual subspace re-projection network for surface anomaly detection](https://mlsanity.com/paper/2208.01521/) (2022)
- [MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities](https://mlsanity.com/paper/2205.00908/) (2022)

## Built on by (most cited first, 30 of 43)

- [Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2502.07601/) (2025)
- [One-for-More: Continual Diffusion Model for Anomaly Detection](https://mlsanity.com/paper/2502.19848/) (2025)
- [SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection](https://mlsanity.com/paper/2408.03143/) (2024)
- [Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2503.19357/) (2025)
- [Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning](https://mlsanity.com/paper/2508.02293/) (2025)
- [Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection](https://mlsanity.com/paper/2510.16865/) (2025)
- [SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling](https://mlsanity.com/paper/2602.23013/) (2026)
- [A Survey on Foundation-Model-Based Industrial Defect Detection](https://mlsanity.com/paper/2502.19106/) (2025)
- [No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes](https://mlsanity.com/paper/2508.19060/) (2025)
- [SALAD -- Semantics-Aware Logical Anomaly Detection](https://mlsanity.com/paper/2509.02101/) (2025)
- [AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors](https://mlsanity.com/paper/2601.20524/) (2026)
- [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)
- [RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images](https://mlsanity.com/paper/2505.21152/) (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)
- [A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection](https://mlsanity.com/paper/2603.25159/) (2026)
- [Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection](https://mlsanity.com/paper/2412.00560/) (2024)
- [Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection](https://mlsanity.com/paper/2508.03539/) (2025)
- [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)
- [Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection](https://mlsanity.com/paper/2503.23451/) (2025)
- [MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection](https://mlsanity.com/paper/2504.12970/) (2025)
- [3D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturing](https://mlsanity.com/paper/2507.07838/) (2025)
- [Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts](https://mlsanity.com/paper/2507.16946/) (2025)
- [ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining](https://mlsanity.com/paper/2511.05245/) (2025)
- [Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion](https://mlsanity.com/paper/2601.08022/) (2026)
- [Multimodal Industrial Anomaly Detection via Geometric Prior](https://mlsanity.com/paper/2603.22757/) (2026)
- [Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection](https://mlsanity.com/paper/2605.26468/) (2026)
- [Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks](https://mlsanity.com/paper/2606.02042/) (2026)

## Abstract (arXiv)

Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network followed by a discriminative network that relies on the reconstruction output. Currently used reconstructive networks often produce poor reconstructions that either still contain anomalies or lack details in anomaly-free regions. Discriminative methods are robust to some reconstructive network failures, suggesting that the discriminative network learns a strong normal appearance signal that the reconstructive networks miss. We reformulate the two-stage architecture into a single-stage iterative process that allows the exchange of information between the reconstruction and localization. We propose a novel transparency-based diffusion process where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately while maintaining the appearance of anomaly-free regions using localization cues of previous steps. We implement the proposed process as TRANSparency DifFUSION (TransFusion), a novel discriminative anomaly detection method that achieves state-of-the-art performance on both the VisA and the MVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively. Code: https://github.com/MaticFuc/ECCV_TransFusion