Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might limit their adaptability to an increasing set of anomaly detection tasks due to the priors in the selection of auxiliary datasets or the strategy of anomaly simulation. To tackle this challenge, we first introduce a prior-less anomaly generation paradigm and subsequently develop an innovative unsupervised anomaly detection framework named GRAD, grounded in this paradigm. GRAD comprises three essential components: (1) a diffusion model (PatchDiff) to generate contrastive patterns by preserving the local structures while disregarding the global structures present in normal images, (2) a self-supervised reweighting mechanism to handle the challenge of long-tailed and unlabeled contrastive patterns generated by PatchDiff, and (3) a lightweight patch-level detector to efficiently distinguish the normal patterns and reweighted contrastive patterns. The generation results of PatchDiff effectively expose various types of anomaly patterns, e.g. structural and logical anomaly patterns. In addition, extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed.

翻译：近期无监督异常检测方法通常依赖辅助数据集预训练的特征提取器或精心设计的异常模拟样本。然而，这可能会因辅助数据集选择或异常模拟策略的先验知识，限制其应对日益增多的异常检测任务的适应性。为攻克这一难题，我们首先提出一种无先验的异常生成范式，并基于该范式开发了创新的无监督异常检测框架GRAD。GRAD包含三个核心组件：(1) 扩散模型PatchDiff，通过保留正常图像的局部结构而忽略全局结构，生成对比模式；(2) 自监督重加权机制，用于处理PatchDiff生成的长尾且未标注的对比模式挑战；(3) 轻量级块级检测器，实现正常模式与重加权对比模式的高效区分。PatchDiff的生成结果有效暴露了多种异常模式类型，例如结构异常和逻辑异常模式。此外，在MVTec AD和MVTec LOCO数据集上的大量实验也支持上述观察结果，并证明GRAD达到了具有竞争力的异常检测精度和优越的推理速度。