提出持续学习的扩散模型，解决异常检测中记忆遗忘与幻觉问题。
One-for-More: Continual Diffusion Model for Anomaly Detection
- 用梯度投影实现稳定持续学习，保护已学知识
- 在MVTec和VisA上17/18任务排名第一
- 创新性引入异常掩码网络，提升条件生成能力
随着生成模型的发展，越来越多研究尝试将各类任务统一到生成框架中。异常检测方法也采用扩散模型，在给定任意异常图像时生成或重建正常样本。然而，我们发现扩散模型存在严重的“忠实性幻觉”和“灾难性遗忘”问题，难以应对不可预测的模式增量。为此，提出一种持续扩散模型，利用梯度投影实现稳定持续学习：通过修改梯度方向来正则化模型更新，保护已有知识。但该方法伴随马尔可夫过程带来的高内存开销。因此，提出基于线性表示传递性的迭代奇异值分解方法，仅需极少内存且几乎无性能损失。最后，针对扩散模型对正常图像过拟合的风险，设计异常掩码网络以增强条件机制。在持续异常检测任务中，本方法在MVTec和VisA数据集上17/18设置下取得第一。代码已公开于https://github.com/FuNz-0/One-for-More。
原文摘要 · Abstract (English)
With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. However, our study found that the diffusion model suffers from severe ``faithfulness hallucination'' and ``catastrophic forgetting'', which can't meet the unpredictable pattern increments. To mitigate the above problems, we propose a continual diffusion model that uses gradient projection to achieve stable continual learning. Gradient projection deploys a regularization on the model updating by modifying the gradient towards the direction protecting the learned knowledge. But as a double-edged sword, it also requires huge memory costs brought by the Markov process. Hence, we propose an iterative singular value decomposition method based on the transitive property of linear representation, which consumes tiny memory and incurs almost no performance loss. Finally, considering the risk of ``over-fitting'' to normal images of the diffusion model, we propose an anomaly-masked network to enhance the condition mechanism of the diffusion model. For continual anomaly detection, ours achieves first place in 17/18 settings on MVTec and VisA. Code is available at https://github.com/FuNz-0/One-for-More
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