论文标题
OCMST：使用卷积神经网络和最小跨越树木的一级新颖性检测
OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees
论文作者
论文摘要
我们提出了一个新的模型，称为“一类最小跨越树（OCMST）”，用于新颖的检测问题，该问题使用卷积神经网络（CNN）作为基于最小生成树（MST）的基于图形提取器和基于图的模型。在新颖的检测方案中，训练数据没有受到异常值（异常类别）的污染，目标是认识到测试实例是否属于正常类或异常类别。我们的方法使用CNN的深度功能来喂养从每个测试实例开始构建的一对MST。为了减少计算时间，我们使用参数$γ$来指定MST的大小从测试实例开始到邻居。为了证明拟议方法的有效性，我们对两个公开可用的数据集进行了实验，这些数据集在文献中众所周知，我们在CIFAR10数据集上实现了最新结果。
We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to recognize if a test instance belongs to the normal class or to the abnormal class. Our approach uses the deep features from CNN to feed a pair of MSTs built starting from each test instance. To cut down the computational time we use a parameter $γ$ to specify the size of the MST's starting to the neighbours from the test instance. To prove the effectiveness of the proposed approach we conducted experiments on two publicly available datasets, well-known in literature and we achieved the state-of-the-art results on CIFAR10 dataset.
