OCmst：基于卷积神经网络和最小生成树的一类新颖性检测
OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees
摘要：
我们提出了一种新的模型称为一类最小生成树（OCmst）的新颖性检测问题，使用卷积神经网络（CNN）作为深度特征提取器和基于最小生成树（MST）的基于图的模型。在新颖性检测场景中，训练数据不被离群值（异常类）污染，并且目标是识别测试实例属于正常类还是异常类。我们的方法使用来自CNN的深层特征来提供从每个测试实例开始构建的一对MST。为了减少计算时间，我们使用一个参数$γ$来指定从测试实例开始到邻居的MST的大小。为了证明所提出的方法的有效性，我们在两个公开可用的数据集上进行了实验，在文献中众所周知，我们在CIFAR10数据集上取得了最先进的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
