{"title":"Semi-supervised noise-resilient anomaly detection with feature autoencoder","authors":"","doi":"10.1016/j.knosys.2024.112445","DOIUrl":null,"url":null,"abstract":"<div><p>Most methods only use normal samples to learn anomaly detection (AD) models in an unsupervised manner. However, these samples may be noisy in real-world applications, causing the models to be unable to accurately identify anomaly objects. In addition, there are a small number of anomaly samples in real industrial production that should be fully utilized to help model discrimination. Existing methods of introducing anomaly samples still have bottlenecks in model identification capabilities. In this paper, by introducing both normal and a few abnormal samples, we propose a novel semi-supervised learning method for anomaly detection, named <em>RobustPatch</em>, which can improve the model discriminability through a self-cross scoring mechanism and the learning of feature AutoEncoder. Our approach contains two core designs: Firstly, we propose a self-cross scoring module, calculating the weights of normal and anomaly features extracted from corresponding images using a self-scoring and cross-scoring manner, respectively. Secondly, our approach proposes a fully connected feature AutoEncoder to rate the extracted features, which is trained with the supervision of the scored weights. Extensive experiments on the MVTecAD and BTAD datasets validate the superior anomaly boundaries discriminability of our approach and superior performance in noise-polluted scenarios.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124010797","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Most methods only use normal samples to learn anomaly detection (AD) models in an unsupervised manner. However, these samples may be noisy in real-world applications, causing the models to be unable to accurately identify anomaly objects. In addition, there are a small number of anomaly samples in real industrial production that should be fully utilized to help model discrimination. Existing methods of introducing anomaly samples still have bottlenecks in model identification capabilities. In this paper, by introducing both normal and a few abnormal samples, we propose a novel semi-supervised learning method for anomaly detection, named RobustPatch, which can improve the model discriminability through a self-cross scoring mechanism and the learning of feature AutoEncoder. Our approach contains two core designs: Firstly, we propose a self-cross scoring module, calculating the weights of normal and anomaly features extracted from corresponding images using a self-scoring and cross-scoring manner, respectively. Secondly, our approach proposes a fully connected feature AutoEncoder to rate the extracted features, which is trained with the supervision of the scored weights. Extensive experiments on the MVTecAD and BTAD datasets validate the superior anomaly boundaries discriminability of our approach and superior performance in noise-polluted scenarios.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.