title: Few-Shot Anomaly-Driven Generation for Anomaly Classification and
    Segmentation
summary: Anomaly detection is a practical and challenging task due to the scarcity of
  anomaly samples in industrial inspection. Some existing anomaly detection
  methods address this issue by synthesizing anomalies with noise or external
  data. However, there is always a large semantic gap between synthetic and
  real-world anomalies, resulting in weak performance in anomaly detection. To
  solve the problem, we propose a few-shot Anomaly-driven Generation (AnoGen)
  method, which guides the diffusion model to generate realistic and diverse
  anomalies with only a few real anomalies, thereby benefiting training anomaly
  detection models. Specifically, our work is divided into three stages. In the
  first stage, we learn the anomaly distribution based on a few given real
  anomalies and inject the learned knowledge into an embedding. In the second
  stage, we use the embedding and given bounding boxes to guide the diffusion
  model to generate realistic and diverse anomalies on specific objects (or
  textures). In the final stage, we propose a weakly-supervised anomaly detection
  method to train a more powerful model with generated anomalies. Our method
  builds upon DRAEM and DesTSeg as the foundation model and conducts experiments
  on the commonly used industrial anomaly detection dataset, MVTec. The
  experiments demonstrate that our generated anomalies effectively improve the
  model performance of both anomaly classification and segmentation tasks
  simultaneously, \eg, DRAEM and DseTSeg achieved a 5.8\% and 1.5\% improvement
  in AU-PR metric on segmentation task, respectively. The code and generated
  anomalous data are available at https://github.com/gaobb/AnoGen.
paper_authors._id: 682549a75b5784e023ed7d8a
paper_authors.name: Guan Gui
paper_authors._id: 682549a75b5784e023ed7d8b
paper_authors.name: Bin-Bin Gao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-15T10:31:36.508Z
paper_authors._id: 648972ff99f6c45ff6bbd295
paper_authors.avatarUrl: /avatars/d1fe9d631a786462d55cfe43c565c88e.svg
paper_authors.fullname: Bin-Bin Gao
paper_authors.type: user
paper_authors.user: csgaobb
paper_authors._id: 682549a75b5784e023ed7d8c
paper_authors.name: Jun Liu
paper_authors._id: 682549a75b5784e023ed7d8d
paper_authors.name: Chengjie Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:22:17.616Z
paper_authors._id: 65729087379284b904a6d81d
paper_authors.avatarUrl: /avatars/0e7968b1d9221d523a54e3ac787e449d.svg
paper_authors.fullname: Chengjie Wang
paper_authors.type: user
paper_authors.user: chengjie-wang
paper_authors._id: 682549a75b5784e023ed7d8e
paper_authors.name: Yunsheng Wu
paper_title: Few-Shot Anomaly-Driven Generation for Anomaly Classification and
    Segmentation
paper_summary: Anomaly detection is a practical and challenging task due to the scarcity of
  anomaly samples in industrial inspection. Some existing anomaly detection
  methods address this issue by synthesizing anomalies with noise or external
  data. However, there is always a large semantic gap between synthetic and
  real-world anomalies, resulting in weak performance in anomaly detection. To
  solve the problem, we propose a few-shot Anomaly-driven Generation (AnoGen)
  method, which guides the diffusion model to generate realistic and diverse
  anomalies with only a few real anomalies, thereby benefiting training anomaly
  detection models. Specifically, our work is divided into three stages. In the
  first stage, we learn the anomaly distribution based on a few given real
  anomalies and inject the learned knowledge into an embedding. In the second
  stage, we use the embedding and given bounding boxes to guide the diffusion
  model to generate realistic and diverse anomalies on specific objects (or
  textures). In the final stage, we propose a weakly-supervised anomaly detection
  method to train a more powerful model with generated anomalies. Our method
  builds upon DRAEM and DesTSeg as the foundation model and conducts experiments
  on the commonly used industrial anomaly detection dataset, MVTec. The
  experiments demonstrate that our generated anomalies effectively improve the
  model performance of both anomaly classification and segmentation tasks
  simultaneously, \eg, DRAEM and DseTSeg achieved a 5.8\% and 1.5\% improvement
  in AU-PR metric on segmentation task, respectively. The code and generated
  anomalous data are available at https://github.com/gaobb/AnoGen.
paper_ai_summary: A few-shot Anomaly-driven Generation method improves anomaly detection by guiding diffusion models to create realistic, diverse anomalies from limited real samples, enhancing both classification and segmentation.
paper_ai_keywords: few-shot Anomaly-driven Generation AnoGen diffusion model anomaly distribution embedding bounding boxes weakly-supervised anomaly detection DRAEM DesTSeg MVTec AU-PR metric