Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralque...
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ISBN:
(数字)9783540476986
ISBN:
(纸本)9783540476979
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this paper, we propose a dual teacher–student-based discrimination model (DT...
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The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this paper, we propose a dual teacher–student-based discrimination model (DTSD) for anomaly detection, which combines the advantages of both embedding-based and reconstruction-based methods. First, the DTSD builds a dual teacher-student architecture consisting of a pretrained teacher encoder with frozen parameters, a student encoder and a student decoder. By distillation of knowledge from the teacher encoder, the two teacher-student modules acquire the ability to capture both local and global anomaly patterns. Second, to address the issue of poor reconstruction quality faced by previous reconstruction-based approaches in some challenging cases, the model employs a feature bank that stores encoded features of normal samples. By incorporating template features from the feature bank, the student decoder receives explicit guidance to enhance the quality of reconstruction. Finally, a segmentation network is utilized to adaptively integrate multiscale anomaly information from the two teacher–student modules, thereby improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. The code of DTSD is publicly available on https://***/Math-Computer/DTSD.
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