用扩散模型生成历史数据，解决持续异常检测中的遗忘与细粒度分割难题。
ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection
- 利用扩散模型的语义和空间特征压缩历史数据，生成高保真复现样本。
- 在VisA和MVTec上分割性能分别提升11.5%和8.1%，显著优于现有方法。
- 适合需要长期学习新异常类且要求精准定位的工业缺陷检测场景。
持续异常检测（CAD）使模型在学习新类别时保留对历史类别的知识。其面临两大挑战：灾难性遗忘和小异常区域的分割困难。现有方法通过存储图像分布或补丁特征缓解遗忘问题，但难以保留像素级细节以实现准确分割。为此，我们提出ReplayCAD，一种基于扩散模型的生成式回放框架，通过高质量历史数据重播有效保留像素级细节。具体地，我们在预训练扩散模型的条件空间中搜索类别语义嵌入，引导模型生成具备精细像素细节的数据，从而提升分割性能。然而，仅依赖语义特征导致空间多样性有限，因此我们进一步引入空间特征指导数据压缩，实现对样本空间的精确控制，生成更具多样性的数据。该方法在分类与分割任务上均达到当前最优表现，分割性能在VisA上提升11.5%，MVTec上提升8.1%。代码已开源。
原文摘要 · Abstract (English)
Continual Anomaly Detection (CAD) enables anomaly detection models in learning new classes while preserving knowledge of historical classes. CAD faces two key challenges: catastrophic forgetting and segmentation of small anomalous regions. Existing CAD methods store image distributions or patch features to mitigate catastrophic forgetting, but they fail to preserve pixel-level detailed features for accurate segmentation. To overcome this limitation, we propose ReplayCAD, a novel diffusion-driven generative replay framework that replay high-quality historical data, thus effectively preserving pixel-level detailed features. Specifically, we compress historical data by searching for a class semantic embedding in the conditional space of the pre-trained diffusion model, which can guide the model to replay data with fine-grained pixel details, thus improving the segmentation performance. However, relying solely on semantic features results in limited spatial diversity. Hence, we further use spatial features to guide data compression, achieving precise control of sample space, thereby generating more diverse data. Our method achieves state-of-the-art performance in both classification and segmentation, with notable improvements in segmentation: 11.5% on VisA and 8.1% on MVTec. Our source code is available at https://github.com/HULEI7/ReplayCAD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。