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Deep Learning Theory and Applications: First International Conference, DeLTA 2020, Virtual Event, July 8-10, 2020, and Second International Conference, DeLTA 2021, Virtual Event, July 79, 2021, Revised Selected Papers 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 151 pages, kõrgus x laius: 235x155 mm, kaal: 261 g, 51 Illustrations, color; 20 Illustrations, black and white; XI, 151 p. 71 illus., 51 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1854
  • Ilmumisaeg: 07-Jul-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031373197
  • ISBN-13: 9783031373190
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Deep Learning Theory and Applications: First International Conference, DeLTA 2020, Virtual Event, July 8-10, 2020, and Second International Conference, DeLTA 2021, Virtual Event, July 79, 2021, Revised Selected Papers 1st ed. 2023Suurem pilt
  • Formaat: Paperback / softback, 151 pages, kõrgus x laius: 235x155 mm, kaal: 261 g, 51 Illustrations, color; 20 Illustrations, black and white; XI, 151 p. 71 illus., 51 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1854
  • Ilmumisaeg: 07-Jul-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031373197
  • ISBN-13: 9783031373190
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031373190.html
Märksõnad:
This 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.
Alternative 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.