论文标题
通过建模感知特征来定位异常
Anomaly localization by modeling perceptual features
论文作者
论文摘要
尽管使用变异自动编码器（VAE）对图像数据集进行了无监督的生成建模，但已用于检测异常图像或图像中的异常区域，但最近的作品表明，这种方法经常显示与人类感知不一致的图像或区域，甚至质疑具有强大型模型的可用性模型的可用性。在这里，我们认为这些问题可以从对异常分布的简单模型中出现，我们提出了一个新的基于VAE的模型，该模型表达了一个更复杂的异常模型，该模型也更接近人类的感知。此功能增强的VAE不仅是通过在像素空间中重建输入图像的，而且在几个不同的特征空间中通过在大图像数据集上训练的卷积神经网络计算而训练。它对MVTEC异常检测和本地化数据集的最先进方法明显改进。
Although unsupervised generative modeling of an image dataset using a Variational AutoEncoder (VAE) has been used to detect anomalous images, or anomalous regions in images, recent works have shown that this method often identifies images or regions that do not concur with human perception, even questioning the usability of generative models for robust anomaly detection. Here, we argue that those issues can emerge from having a simplistic model of the anomaly distribution and we propose a new VAE-based model expressing a more complex anomaly model that is also closer to human perception. This Feature-Augmented VAE is trained by not only reconstructing the input image in pixel space, but also in several different feature spaces, which are computed by a convolutional neural network trained beforehand on a large image dataset. It achieves clear improvement over state-of-the-art methods on the MVTec anomaly detection and localization datasets.
