论文标题
使用预训练的网络完全无监督的视觉检查的数据完善
Data refinement for fully unsupervised visual inspection using pre-trained networks
论文作者
论文摘要
最近在视觉检查领域取得了巨大进展。更具体地说,已经证明,在由深度训练的神经网络提取的特征上使用经典异常检测技术可在MVTEC异常检测(MVTEC AD)数据集上提供出色的性能。但是,像大多数其他异常检测策略一样,这些预训练的方法假设所有训练数据都是正常的。结果,它们不能被认为是完全无监督的。据我们所知,没有工作在完全无监督的环境下研究这些预训练的方法。在这项工作中,我们首先使用污染的训练集(即包含有缺陷的样品)评估这些预训练的方法对完全无监督的环境的鲁棒性,并表明与诸如Cutpaste之类的方法相比,这些方法对污染更为强大。然后,我们提出SROC,这是一个简单的完善策略,用于一个类别的分类。 SROC使能够从训练集中删除大多数污染图像,并恢复一些丢失的AUC。我们进一步表明,我们的简单启发式竞争与现有文献的竞争更为复杂,甚至超过了更复杂的策略。
Anomaly detection has recently seen great progress in the field of visual inspection. More specifically, the use of classical outlier detection techniques on features extracted by deep pre-trained neural networks have been shown to deliver remarkable performances on the MVTec Anomaly Detection (MVTec AD) dataset. However, like most other anomaly detection strategies, these pre-trained methods assume all training data to be normal. As a consequence, they cannot be considered as fully unsupervised. There exists to our knowledge no work studying these pre-trained methods under fully unsupervised setting. In this work, we first assess the robustness of these pre-trained methods to fully unsupervised context, using polluted training sets (i.e. containing defective samples), and show that these methods are more robust to pollution compared to methods such as CutPaste. We then propose SROC, a Simple Refinement strategy for One Class classification. SROC enables to remove most of the polluted images from the training set, and to recover some of the lost AUC. We further show that our simple heuristic competes with, and even outperforms much more complex strategies from the existing literature.