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Generative Adversial Networks. A 2019 Review

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    SYSNO ASEP0518921
    Document TypeA - Abstract
    R&D Document TypeThe record was not marked in the RIV
    R&D Document TypeNení vybrán druh dokumentu
    TitleGenerative Adversial Networks. A 2019 Review
    Author(s) Coufal, David (UIVT-O) RID, SAI, ORCID
    Source TitleProceedings of the 22nd Czech-Japan Seminar on Data Analysis and Decision Making (CJS’19). - Praha : MatfyzPress, 2019 / Inuiguchi Masahiro ; Jiroušek Radim ; Kratochvíl Václav - ISBN 978-80-7378-400-3
    S. 25-27
    Number of pages3 s.
    ActionCJS 2019. Czech-Japan Seminar on Data Analysis and Decision Making /22./
    Event date25.09.2019 - 28.09.2019
    VEvent locationNový Světnov
    CountryCZ - Czech Republic
    Event typeWRD
    Languageeng - English
    CountryCZ - Czech Republic
    R&D ProjectsGA18-23827S GA ČR - Czech Science Foundation (CSF)
    Institutional supportUIVT-O - RVO:67985807
    AnnotationThe generative adversial networks (GANs) represent an exciting concept that lies at the borderline between probabilistic modeling and machine learning. Despite the short history (since 2014) the GANs have caused a dramatic shift in several machine learning fields, however, mostly in generating photorealistic pictures of different objects. The basic idea the GANs are based on is conceptually simple, however, concrete implementations are still partly an art rather than established science and are tightly interconnected with programming in deep-learning frameworks such as TensorFlow or PyTorch to mention the most popular ones. The purpose of this contribution is to present a brief review of the GAN implementations milestones that paved the way to their current success. The list is not exhaustive as hundreds of implementations are available in several GANs ZOOs, see e.g., [5], but we try to be representative.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová,, Tel.: 266 053 800
    Year of Publishing2020
Number of the records: 1