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Supervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2021-03-03 , DOI: 10.1142/s0218001421520108
Claudio Piciarelli 1 , Pankaj Mishra 1 , Gian Luca Foresti 1
Affiliation  

Detecting anomalous patterns in data is a relevant task in many practical applications, such as defective items detection in industrial inspection systems, cancer identification in medical images, or attacker detection in network intrusion detection systems. This paper focuses on detection of anomalous images, this is images that visually deviate from a reference set of regular data. While anomaly detection has been widely studied in the context of classical machine learning, the application of modern deep learning techniques in this field is still limited. We here propose a capsule-based network for anomaly detection in an extremely imbalanced fully supervised context: we assume that anomaly samples are available, but their amount is limited if compared to regular data. By using a variant of the standard CapsNet architecture, we achieved state-of-the-art results on the MNIST, F-MNIST and K-MNIST datasets.

中文翻译:

使用胶囊网络对高度不平衡数据集进行监督异常检测

检测数据中的异常模式是许多实际应用中的相关任务,例如工业检测系统中的缺陷物品检测、医学图像中的癌症识别或网络入侵检测系统中的攻击者检测。本文重点关注异常图像的检测,这是在视觉上偏离常规数据参考集的图像。虽然异常检测已在经典机器学习的背景下得到广泛研究,但现代深度学习技术在该领域的应用仍然有限。我们在这里提出了一个基于胶囊的网络,用于在极度不平衡的完全监督环境中进行异常检测:我们假设异常样本是可用的,但与常规数据相比,它们的数量是有限的。通过使用标准 CapsNet 架构的变体,
更新日期:2021-03-03
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