论文标题
通过聚集深层锥体表示图像异常检测
Image Anomaly Detection by Aggregating Deep Pyramidal Representations
论文作者
论文摘要
异常检测在于在数据集中识别与大多数数据显着不同的样品，代表正常类。它有许多实际应用，例如从工业系统中的产品检测到有缺陷的医疗成像。本文着重于使用具有多个金字塔水平的深神经网络进行图像异常检测，以分析不同尺度的图像特征。我们建议使用标准卷积自动编码器进行基于编码编码方案的网络，该网络仅对正常数据进行培训，以构建正态性模型。可以通过网络重建其输入的能力来检测异常。实验结果表明，MNIST，FMNIST和最近的MVTEC异常检测数据集具有良好的准确性
Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset
