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Generative Adversial Networks. A 2019 Review
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SYSNO ASEP 0518921 Druh ASEP A - Abstrakt Zařazení RIV Záznam nebyl označen do RIV Zařazení RIV Není vybrán druh dokumentu Název Generative Adversial Networks. A 2019 Review Tvůrce(i) Coufal, David (UIVT-O) RID, SAI, ORCID Zdroj.dok. Proceedings 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. 25-27Poč.str. 3 s. Akce CJS 2019. Czech-Japan Seminar on Data Analysis and Decision Making /22./ Datum konání 25.09.2019 - 28.09.2019 Místo konání Nový Světnov Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika CEP GA18-23827S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 Anotace The 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020
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