Anomaly detection without priors of the anomalies is challenging. In the
field of unsupervised anomaly detection, traditional auto-encoder (AE) tends to
fail based on the assumption that by training only on normal images, the model
will not be able to reconstruct abnormal images correctly. On the contrary, we
propose a novel patch-wise auto-encoder (Patch AE) framework, which aims at
enhancing the reconstruction ability of AE to anomalies instead of weakening
it. Each patch of image is reconstructed by corresponding spatially distributed
feature vector of the learned feature representation, i.e., patch-wise
reconstruction, which ensures anomaly-sensitivity of AE. Our method is simple
and efficient. It advances the state-of-the-art performances on Mvtec AD
benchmark, which proves the effectiveness of our model. It shows great
potential in practical industrial application scenarios.

无先验异常检测是具有挑战性的。我们提出了一种新型的分块自编码器（Patch AE）框架，旨在增强自编码器对异常图像的重构能力而不是削弱。通过对学习到的特征表示的空间分布特征向量的相应位置进行重构，即分块重构，确保了自编码器对异常的敏感性。我们的方法简单高效，在 Mvtec AD 基准方面取得了最新的研究成果，证明了我们模型的有效性。它在实际工业应用场景中显示出巨大的潜力。

面板自编码器用于图像异常检测

Patch-wise Auto-Encoder for Visual Anomaly Detection

We propose a simple and application-friendly network (called SimpleNet) for
detecting and localizing anomalies. SimpleNet consists of four components: (1)
a pre-trained Feature Extractor that generates local features, (2) a shallow
Feature Adapter that transfers local features towards target domain, (3) a
simple Anomaly Feature Generator that counterfeits anomaly features by adding
Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that
distinguishes anomaly features from normal features. During inference, the
Anomaly Feature Generator would be discarded. Our approach is based on three
intuitions. First, transforming pre-trained features to target-oriented
features helps avoid domain bias. Second, generating synthetic anomalies in
feature space is more effective, as defects may not have much commonality in
the image space. Third, a simple discriminator is much efficient and practical.
In spite of simplicity, SimpleNet outperforms previous methods quantitatively
and qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomaly
detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best
performing model. Furthermore, SimpleNet is faster than existing methods, with
a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet
demonstrates significant improvements in performance on the One-Class Novelty
Detection task. Code: this https URL

本文提出一种简单且应用友好的神经网络（简称 SimpleNet），用于检测和定位异常。SimpleNet 由四个组件组成：预训练特征提取器、浅层特征适配器、简单异常特征生成器和二进制异常鉴别器，并在 MVTec AD 基准测试上实现了 99.6％的异常检测 AUROC。