Image anomaly detection is an application-driven problem where the aim is to identify novel samples, which differ significantly from the normal ones. We here propose Pyramidal Image Anomaly DEtector (PIADE), a deep reconstruction-based pyramidal approach, in which image features are extracted at different scale levels to better catch the peculiarities that could help to discriminate between normal and anomalous data. The features are dynamically routed to a reconstruction layer and anomalies can be identified by comparing the input image with its reconstruction. Unlike similar approaches, the comparison is done by using structural similarity and perceptual loss rather than trivial pixel-by-pixel comparison. The proposed method performed at par or better than the state-of-the-art methods when tested on publicly available datasets such as CIFAR10, COIL-100 and MVTec.

中文翻译：

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具有深度金字塔表示和动态路由的图像异常检测神经网络

图像异常检测是一个应用驱动的问题，其目的是识别与正常样本显着不同的新样本。我们在这里提出了金字塔图像异常检测器 (PIADE)，这是一种基于深度重建的金字塔方法，其中在不同的尺度级别上提取图像特征，以更好地捕捉有助于区分正常和异常数据的特性。这些特征被动态路由到重建层，并且可以通过将输入图像与其重建进行比较来识别异常。与类似的方法不同，比较是通过使用结构相似性和感知损失而不是微不足道的逐像素比较来完成的。在 CIFAR10 等公开可用的数据集上进行测试时，所提出的方法的性能达到或优于最先进的方法，