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.

本文提出了一种基于 Feature-Augmented VAE 的复杂异常模型的新 VAE 模型，该模型不仅在像素空间中重构输入图像，而且还在多个不同的特征空间中进行重构，通过对先前训练的卷积神经网络进行计算，对 MVTec 异常检测和定位数据集的表现明显优于现有最先进的方法。

通过建立感知特征模型进行异常检测定位

Anomaly localization by modeling perceptual features

We introduce a powerful student-teacher framework for the challenging problem
of unsupervised anomaly detection and pixel-precise anomaly segmentation in
high-resolution images. Student networks are trained to regress the output of a
descriptive teacher network that was pretrained on a large dataset of patches
from natural images. This circumvents the need for prior data annotation.
Anomalies are detected when the outputs of the student networks differ from
that of the teacher network. This happens when they fail to generalize outside
the manifold of anomaly-free training data. The intrinsic uncertainty in the
student networks is used as an additional scoring function that indicates
anomalies. We compare our method to a large number of existing deep learning
based methods for unsupervised anomaly detection. Our experiments demonstrate
improvements over state-of-the-art methods on a number of real-world datasets,
including the recently introduced MVTec Anomaly Detection dataset that was
specifically designed to benchmark anomaly segmentation algorithms.

使用学生 - 老师框架，通过深度学习方法实现无监督异常检测和像素级别的异常分割，并在多个实验数据集上展示了显著的改进效果。