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Generative Adversial Networks. A 2019 Review
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SYSNO ASEP 0518921 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title Generative Adversial Networks. A 2019 Review Author(s) Coufal, David (UIVT-O) RID, SAI, ORCID Source Title 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. 25-27Number of pages 3 s. Action CJS 2019. Czech-Japan Seminar on Data Analysis and Decision Making /22./ Event date 25.09.2019 - 28.09.2019 VEvent location Nový Světnov Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic R&D Projects GA18-23827S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020
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