001471651 000__ 04259cam\\2200685\i\4500
001471651 001__ 1471651
001471651 003__ OCoLC
001471651 005__ 20230908003308.0
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001471651 007__ cr\cn\nnnunnun
001471651 008__ 230712s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001471651 020__ $$a9783031373206$$q(electronic bk.)
001471651 020__ $$a3031373200$$q(electronic bk.)
001471651 020__ $$z9783031373190
001471651 0247_ $$a10.1007/978-3-031-37320-6$$2doi
001471651 035__ $$aSP(OCoLC)1390129508
001471651 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001471651 049__ $$aISEA
001471651 050_4 $$aQ325.73
001471651 08204 $$a006.3/1$$223/eng/20230712
001471651 1112_ $$aInternational Conference on Deep Learning Theory and Applications$$n(1st :$$d2020 :$$cOnline).
001471651 24510 $$aDeep learning theory and applications :$$bFirst International Conference, DeLTA 2020, virtual event, July 8-10, 2020, and Second International Conference, DeLTA 2021, virtual event, July 7–9, 2021, revised selected papers /$$cAna Fred, Carlo Sansone, Kurosh Madani, editors.
001471651 24630 $$aDeLTA 2020
001471651 24630 $$aDeLTA 2021
001471651 250__ $$a1st ed. 2023.
001471651 264_1 $$aCham :$$bSpringer,$$c[2023]
001471651 264_4 $$c©2023
001471651 300__ $$a1 online resource (xi, 151 pages) :$$billustrations (chiefly color).
001471651 336__ $$atext$$btxt$$2rdacontent
001471651 337__ $$acomputer$$bc$$2rdamedia
001471651 338__ $$aonline resource$$bcr$$2rdacarrier
001471651 4901_ $$aCommunications in computer and information science,$$x1865-0937 ;$$v1854
001471651 500__ $$aInternational conference proceedings.
001471651 500__ $$aIncludes author index.
001471651 5050_ $$aAlternative Data Augmentation for Industrial Monitoring using Adversarial Learning -- Multi-stage Conditional GAN Architectures for Person-image Generation -- Evaluating Deep Learning Models for the Automatic Inspection of Collective Protective Equipment -- Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks -- Forecasting the UN Sustainable Development Goals -- Disrupting Active Directory Attacks with Deep Learning for Organic Honeyuser Placement -- Crack Detection on Brick Walls by Convolutional Neural Networks using the Methods of Sub-Dataset Generation and Matching.
001471651 506__ $$aAccess limited to authorized users.
001471651 520__ $$aThis book constitutes the refereed post-proceedings of the First International Conference and Second International Conference on Deep Learning Theory and Applications, DeLTA 2020 and DeLTA 2021, was held virtually due to the COVID-19 crisis on July 8-10, 2020 and July 7–9, 2021. The 7 full papers included in this book were carefully reviewed and selected from 58 submissions. They present recent research on machine learning and artificial intelligence in real-world applications such as computer vision, information retrieval and summarization from structuredand unstructured multimodal data sources, natural language understanding andtranslation, and many other application domains.
001471651 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 12, 2023).
001471651 650_0 $$aDeep learning (Machine learning)$$vCongresses.
001471651 655_0 $$aElectronic books.
001471651 655_7 $$aConference papers and proceedings.$$2lcgft
001471651 7001_ $$aFred, Ana,$$eeditor.
001471651 7001_ $$aSansone, Carlo,$$d1969-$$eeditor.
001471651 7001_ $$aMadani, Kurosh,$$eeditor.
001471651 7112_ $$aInternational Conference on Deep Learning Theory and Applications$$n(2nd :$$d2021 :$$cOnline).
001471651 830_0 $$aCommunications in computer and information science ;$$v1854.$$x1865-0937
001471651 852__ $$bebk
001471651 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-37320-6$$zOnline Access$$91397441.1
001471651 909CO $$ooai:library.usi.edu:1471651$$pGLOBAL_SET
001471651 980__ $$aBIB
001471651 980__ $$aEBOOK
001471651 982__ $$aEbook
001471651 983__ $$aOnline
001471651 994__ $$a92$$bISE