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Multi-model anomaly detection for industrial inspection with dynamic loss weighting and soft-hard features loss.

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Title: Multi-model anomaly detection for industrial inspection with dynamic loss weighting and soft-hard features loss.
Authors: Hendria, Willy Fitra1, willy@dagyeom.com, Kim, Hanbi1, hb.kim@dagyeom.com, Seo, Daeho1, dh.seo@dagyeom.com
Source: Neural Computing & Applications; Jul2025, Vol. 37 Issue 21, p17031-17054, 24p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 186779409
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Multi-model anomaly detection for industrial inspection with dynamic loss weighting and soft-hard features loss.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=186779409
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1007/s00521-025-11367-3
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 17031
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      – TitleFull: Multi-model anomaly detection for industrial inspection with dynamic loss weighting and soft-hard features loss.
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            NameFull: Hendria, Willy Fitra
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            NameFull: Kim, Hanbi
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            NameFull: Seo, Daeho
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            – D: 21
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 37
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              Value: 21
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            – TitleFull: Neural Computing & Applications
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