通过增强教师网络提升学生-教师特征金字塔匹配的无监督异常检测方法
Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching
- 用预训练+微调的增强教师网络匹配学生网络特征
- 图像级与像素级准确率分别达0.971和0.977
- 适合需要高精度无监督异常检测的研究者
异常检测是无监督学习中的挑战性课题。本文提出一种学生-教师框架，通过增强教师网络实现高性能。首先在ImageNet上预训练ResNet-18，再在MVTec AD数据集上微调。实验结果表明，该方法在图像级和像素级均优于先前方法。所提模型Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM) 在图像级达到0.971的平均准确率，像素级达到0.977的平均准确率。
原文摘要 · Abstract (English)
Anomaly detection or outlier is one of the challenging subjects in unsupervised learning . This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics . For this purpose , we first pre-train the ResNet-18 network on the ImageNet and then fine-tune it on the MVTech-AD dataset . Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods . Our model , Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM), achieved 0.971 mean accuracy on the image-level and 0.977 mean accuracy on the pixel-level for anomaly detection.
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