Matches in SemOpenAlex for { <https://semopenalex.org/work/W3159415473> ?p ?o ?g. }
- W3159415473 endingPage "108149" @default.
- W3159415473 startingPage "108149" @default.
- W3159415473 abstract "Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets." @default.
- W3159415473 created "2021-05-10" @default.
- W3159415473 creator A5027089733 @default.
- W3159415473 creator A5034562266 @default.
- W3159415473 creator A5040529849 @default.
- W3159415473 creator A5046458978 @default.
- W3159415473 creator A5051253327 @default.
- W3159415473 creator A5058105340 @default.
- W3159415473 creator A5065961805 @default.
- W3159415473 date "2021-07-01" @default.
- W3159415473 modified "2023-10-15" @default.
- W3159415473 title "Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation" @default.
- W3159415473 cites W1968929698 @default.
- W3159415473 cites W2091005538 @default.
- W3159415473 cites W2112796928 @default.
- W3159415473 cites W2129203798 @default.
- W3159415473 cites W2149256602 @default.
- W3159415473 cites W2151854870 @default.
- W3159415473 cites W2160160578 @default.
- W3159415473 cites W2163413582 @default.
- W3159415473 cites W2167240430 @default.
- W3159415473 cites W2296150051 @default.
- W3159415473 cites W2338318698 @default.
- W3159415473 cites W2342408547 @default.
- W3159415473 cites W2560162835 @default.
- W3159415473 cites W2591079949 @default.
- W3159415473 cites W2593414223 @default.
- W3159415473 cites W2597003067 @default.
- W3159415473 cites W2603766943 @default.
- W3159415473 cites W2613548641 @default.
- W3159415473 cites W2734408173 @default.
- W3159415473 cites W2735793369 @default.
- W3159415473 cites W2739007722 @default.
- W3159415473 cites W2755577605 @default.
- W3159415473 cites W2763219399 @default.
- W3159415473 cites W2765811365 @default.
- W3159415473 cites W2767092890 @default.
- W3159415473 cites W2767547957 @default.
- W3159415473 cites W2772330423 @default.
- W3159415473 cites W2803375018 @default.
- W3159415473 cites W2886533716 @default.
- W3159415473 cites W2889191367 @default.
- W3159415473 cites W2890139949 @default.
- W3159415473 cites W2891494317 @default.
- W3159415473 cites W2892335023 @default.
- W3159415473 cites W2901597965 @default.
- W3159415473 cites W2904843110 @default.
- W3159415473 cites W2905027213 @default.
- W3159415473 cites W2906805076 @default.
- W3159415473 cites W2907804426 @default.
- W3159415473 cites W2908004572 @default.
- W3159415473 cites W2909427540 @default.
- W3159415473 cites W2910626581 @default.
- W3159415473 cites W2913608505 @default.
- W3159415473 cites W2913854632 @default.
- W3159415473 cites W2914940294 @default.
- W3159415473 cites W2916832800 @default.
- W3159415473 cites W2921104505 @default.
- W3159415473 cites W2921353139 @default.
- W3159415473 cites W2923951475 @default.
- W3159415473 cites W2929803724 @default.
- W3159415473 cites W2936720645 @default.
- W3159415473 cites W2946584082 @default.
- W3159415473 cites W2951833285 @default.
- W3159415473 cites W2952193749 @default.
- W3159415473 cites W2954996726 @default.
- W3159415473 cites W2959120033 @default.
- W3159415473 cites W2961333734 @default.
- W3159415473 cites W2962883549 @default.
- W3159415473 cites W2962902015 @default.
- W3159415473 cites W2963047971 @default.
- W3159415473 cites W2963079272 @default.
- W3159415473 cites W2963185411 @default.
- W3159415473 cites W2963241379 @default.
- W3159415473 cites W2963241486 @default.
- W3159415473 cites W2963391384 @default.
- W3159415473 cites W2964154860 @default.
- W3159415473 cites W2964277700 @default.
- W3159415473 cites W2964324349 @default.
- W3159415473 cites W2965052638 @default.
- W3159415473 cites W2965385473 @default.
- W3159415473 cites W2966109816 @default.
- W3159415473 cites W2969645632 @default.
- W3159415473 cites W2969841870 @default.
- W3159415473 cites W2973589623 @default.
- W3159415473 cites W2974031746 @default.
- W3159415473 cites W2976029885 @default.
- W3159415473 cites W2980666689 @default.
- W3159415473 cites W2980932224 @default.
- W3159415473 cites W2988327607 @default.
- W3159415473 cites W3004986376 @default.
- W3159415473 cites W3013592437 @default.
- W3159415473 cites W3020687048 @default.
- W3159415473 cites W3103461411 @default.
- W3159415473 cites W3104626566 @default.
- W3159415473 cites W4254041323 @default.
- W3159415473 doi "https://doi.org/10.1016/j.comnet.2021.108149" @default.
- W3159415473 hasPublicationYear "2021" @default.