论文标题
通过训练用污渍形噪声损坏的图像上的自动编码器训练自动编码器，改善了异常检测
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise
论文作者
论文摘要
在工业视觉中，可以使用经过训练的自动编码器来解决异常检测问题，以映射任意图像，即有或没有任何缺陷，将其映射到干净的图像，即没有任何缺陷。在这种方法中，异常检测通常依赖于重建残差或重建不确定性。为了改善重建的清晰度，我们考虑了具有跳过连接的自动编码器体系结构。在只有清洁图像可用于训练的常见情况下，我们建议使用合成噪声模型对其进行损坏，以防止网络趋向于身份映射，并为此目的引入原始的污点噪声模型。我们表明，无论实际缺陷外观如何，该模型都有利于从任意现实世界图像中重建干净的图像。除了证明我们的方法的相关性外，我们的验证还通过比较其对像素和图像异常检测的MVTEC AD数据集的性能，提供了对基于重建方法的首次一致评估。
In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset, both for pixel- and image-wise anomaly detection.
