For a very long time, unsupervised learning for anomaly detection has been at the heart of image processing research and a stepping stone for high performance industrial automation process. With the emergence of CNN, several methods have been proposed such as Autoencoders, GAN, deep feature extraction, etc. In this paper, we propose a new method based on the promising concept of knowledge distillation which consists of training a network (the student) on normal samples while considering the output of a larger pretrained network (the teacher). The main contributions of this paper are twofold: First, a reduced student architecture with optimal layer selection is proposed, then a new Student-Teacher architecture with network bias reduction combining two teachers is proposed in order to jointly enhance the performance of anomaly detection and its localization accuracy. The proposed texture anomaly detector has an outstanding capability to detect defects in any texture and a fast inference time compared to the SOTA methods.

翻译：长期以来，基于无监督学习的异常检测一直是图像处理研究的核心问题，也是高性能工业自动化进程的基石。随着CNN的兴起，研究者提出了多种方法，如自编码器、生成对抗网络、深度特征提取等。本文提出了一种基于知识蒸馏这一前沿概念的新方法，其核心思想是：在利用大型预训练网络（教师网络）输出结果的同时，训练一个学生网络对正常样本进行学习。本文的主要贡献体现在两个方面：首先，提出了一种具有最优层选择的精简学生网络架构；其次，通过融合两个教师网络构建了一种新型师生架构，旨在降低网络偏差的同时协同提升异常检测性能及其定位精度。与当前最优方法相比，所提出的纹理异常检测器不仅具备卓越的纹理缺陷检测能力，同时实现了更快的推理速度。