# Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation

arXiv: https://arxiv.org/abs/2408.13509 · HTML: https://mlsanity.com/paper/2408.13509/

## Key facts

- Cited by 32 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 38 of its 53 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## 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 38)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [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)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020)
- [SimpleNet: A Simple Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2303.15140/) (2023)
- [Few-shot Image Generation via Cross-domain Correspondence](https://mlsanity.com/paper/2104.06820/) (2021)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection](https://mlsanity.com/paper/2403.12580/) (2024)
- [CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization](https://mlsanity.com/paper/2206.04325/) (2022)
- [Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs](https://mlsanity.com/paper/2002.10964/) (2020)
- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [Transparent Image Layer Diffusion using Latent Transparency](https://mlsanity.com/paper/2402.17113/) (2024)
- [AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model](https://mlsanity.com/paper/2312.05767/) (2023)
- [Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2203.14506/) (2022)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter](https://mlsanity.com/paper/2309.02773/) (2023)
- [Explainable Deep Few-shot Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/2108.00462/) (2021)
- [Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation](https://mlsanity.com/paper/2303.02389/) (2023)
- [Prototypical Residual Networks for Anomaly Detection and Localization](https://mlsanity.com/paper/2212.02031/) (2022)

## Built on by (most cited first, 30 of 32)

- [A Survey on Industrial Anomaly Synthesis](https://mlsanity.com/paper/2502.16412/) (2025)
- [Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2510.26464/) (2025)
- [Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection](https://mlsanity.com/paper/2507.09619/) (2025)
- [MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection](https://mlsanity.com/paper/2603.13507/) (2026)
- [Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models](https://mlsanity.com/paper/2604.07802/) (2026)
- [3D-PNAS: 3D Industrial Surface Anomaly Synthesis with Perlin Noise](https://mlsanity.com/paper/2504.12856/) (2025)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment](https://mlsanity.com/paper/2509.06693/) (2025)
- [Universal Few-Shot Spatial Control for Diffusion Models](https://mlsanity.com/paper/2509.07530/) (2025)
- [One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control](https://mlsanity.com/paper/2603.18093/) (2026)
- [IAD-Unify: Task-Specific Interfaces for Industrial Anomaly Understanding, Segmentation, and Generation](https://mlsanity.com/paper/2604.12440/) (2026)
- [Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation](https://mlsanity.com/paper/2605.31266/) (2026)
- [MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness](https://mlsanity.com/paper/2507.02314/) (2025)
- [FaultDiffusion: Few-Shot Fault Time Series Generation with Diffusion Model](https://mlsanity.com/paper/2511.15174/) (2025)
- [Anomaly Detection by Effectively Leveraging Synthetic Images](https://mlsanity.com/paper/2512.23227/) (2025)
- [Double Helix Diffusion for Cross-Domain Anomaly Image Generation](https://mlsanity.com/paper/2509.12787/) (2025)
- [UniADC: A Unified Framework for Anomaly Detection and Classification](https://mlsanity.com/paper/2511.06644/) (2025)
- [DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution](https://mlsanity.com/paper/2511.16920/) (2025)
- [HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2602.09524/) (2026)
- [Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection](https://mlsanity.com/paper/2603.23766/) (2026)
- [Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2604.04658/) (2026)
- [GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis](https://mlsanity.com/paper/2604.08301/) (2026)
- [Large-Scale Universal Defect Generation: Foundation Models and Datasets](https://mlsanity.com/paper/2604.08915/) (2026)
- [Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection](https://mlsanity.com/paper/2605.02438/) (2026)
- [Anomaly-Preference Image Generation](https://mlsanity.com/paper/2605.02439/) (2026)
- [Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection](https://mlsanity.com/paper/2606.25740/) (2026)
- [OSAGEN: Object-Aware Mask Priors and Multistage Decoupled Diffusion for Industrial Anomaly Generation](https://mlsanity.com/paper/2607.29533/) (2026)
- [UniScale: Arbitrary-Scale Industrial Anomaly Generation](https://mlsanity.com/paper/2608.07864/) (2026)
- [DeCo: Zero-Shot Industrial Anomaly Generation through Decoupling and Recoupling](https://mlsanity.com/paper/2608.07904/) (2026)
- [DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation](https://mlsanity.com/paper/2609.02075/) (2026)

## Abstract (arXiv)

The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment the anomaly dataset. However, existing anomaly generation methods suffer from limited diversity in the generated anomalies and struggle to achieve a seamless blending of this anomaly with the original image. Moreover, the generated mask is usually not aligned with the generated anomaly. In this paper, we overcome these challenges from a new perspective, simultaneously generating a pair of the overall image and the corresponding anomaly part. We propose DualAnoDiff, a novel diffusion-based few-shot anomaly image generation model, which can generate diverse and realistic anomaly images by using a dual-interrelated diffusion model, where one of them is employed to generate the whole image while the other one generates the anomaly part. Moreover, we extract background and shape information to mitigate the distortion and blurriness phenomenon in few-shot image generation. Extensive experiments demonstrate the superiority of our proposed model over state-of-the-art methods in terms of diversity, realism and the accuracy of mask. Overall, our approach significantly improves the performance of downstream anomaly inspection tasks, including anomaly detection, anomaly localization, and anomaly classification tasks.