论文标题
TrustMae：使用带有内存的自动编码器具有信任区域的噪声缺陷分类框架
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions
论文作者
论文摘要
在本文中，我们提出了一个称为TrustMae的框架，以解决产品缺陷分类的问题。我们的框架无需依靠难以收集和艰苦的标签的有缺陷的图像，而是可以接受带有未标记图像的数据集。此外，与大多数异常检测方法不同，我们的方法在训练数据集中对噪声或有缺陷的图像具有鲁棒性。我们的框架使用带有内存的自动编码器，其中具有稀疏的内存寻址方案，以避免过度将自动编码器和新颖的信任区域内存更新方案，以使声音远离内存插槽。结果是一个可以重建无缺陷图像并使用感知距离网络识别有缺陷区域的框架。与各种最先进的基线相比，我们的方法在无噪声MVTEC数据集中竞争性能。更重要的是，它在高达40％的噪声水平上保持有效，同时显着优于其他基线。
In this paper, we propose a framework called TrustMAE to address the problem of product defect classification. Instead of relying on defective images that are difficult to collect and laborious to label, our framework can accept datasets with unlabeled images. Moreover, unlike most anomaly detection methods, our approach is robust against noises, or defective images, in the training dataset. Our framework uses a memory-augmented auto-encoder with a sparse memory addressing scheme to avoid over-generalizing the auto-encoder, and a novel trust-region memory updating scheme to keep the noises away from the memory slots. The result is a framework that can reconstruct defect-free images and identify the defective regions using a perceptual distance network. When compared against various state-of-the-art baselines, our approach performs competitively under noise-free MVTec datasets. More importantly, it remains effective at a noise level up to 40% while significantly outperforming other baselines.
