Few-Shot Anomaly-Driven Generation for Anomaly Classification and
  Segmentation
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.
682549a75b5784e023ed7d8a
Guan Gui
682549a75b5784e023ed7d8b
Bin-Bin Gao
claimed_verified
2025-05-15T10:31:36.508Z
648972ff99f6c45ff6bbd295
/avatars/d1fe9d631a786462d55cfe43c565c88e.svg
Bin-Bin Gao
user
csgaobb
682549a75b5784e023ed7d8c
Jun Liu
682549a75b5784e023ed7d8d
Chengjie Wang
admin_assigned
2025-05-16T14:22:17.616Z
65729087379284b904a6d81d
/avatars/0e7968b1d9221d523a54e3ac787e449d.svg
Chengjie Wang
user
chengjie-wang
682549a75b5784e023ed7d8e
Yunsheng Wu
Few-Shot Anomaly-Driven Generation for Anomaly Classification and
  Segmentation
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.
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.
few-shot Anomaly-driven Generation AnoGen diffusion model anomaly distribution embedding bounding boxes weakly-supervised anomaly detection DRAEM DesTSeg MVTec AU-PR metric