Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Marija Jegorova"'
Autor:
Marija Jegorova, Chaitanya Kaul, Charlie Mayor, Alison Q. O'Neil, Alexander Weir, Roderick Murray-Smith, Sotirios A. Tsaftaris
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. :1-20
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f075ced43c962dccd937aae368ce62e0
https://doi.org/10.1109/tpami.2022.3229593
https://doi.org/10.1109/tpami.2022.3229593
Publikováno v:
Marine Technology Society Journal. 55:99-107
In this paper, we present a novel simulation technique for generating high-quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07bbd48199a330a848c8c7dea5a281a7
https://doi.org/10.4031/mtsj.55.4.11
https://doi.org/10.4031/mtsj.55.4.11
Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often addressed with domain adaptation techniques, however the currently existi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb8dcbf06e7f0ab180917c3ef2e00f66
http://arxiv.org/abs/2009.07560
http://arxiv.org/abs/2009.07560
Publikováno v:
IROS
Jegorova, M, Smith, J, Mistry, M & Hospedales, T M 2021, Adversarial Generation of Informative Trajectories for Dynamics System Identification . in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Institute of Electrical and Electronics Engineers (IEEE), pp. 7109-7115, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, United States, 25/10/20 . https://doi.org/10.1109/IROS45743.2020.9340801
Jegorova, M, Smith, J, Mistry, M & Hospedales, T M 2021, Adversarial Generation of Informative Trajectories for Dynamics System Identification . in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Institute of Electrical and Electronics Engineers (IEEE), pp. 7109-7115, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, United States, 25/10/20 . https://doi.org/10.1109/IROS45743.2020.9340801
Dynamic System Identification approaches usually heavily rely on the evolutionary and gradient-based optimisation techniques to produce optimal excitation trajectories for determining the physical parameters of robot platforms. Current optimisation t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cee74d5634bd061a9361b9006a6fb8f
http://arxiv.org/abs/2003.01190
http://arxiv.org/abs/2003.01190
In this paper we present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1199f2316a1d3eac1305ec298d3c8ed9
Publikováno v:
Jegorova, M, Karjalainen, A I, Vazquez, J & Hospedales, T 2020, Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks . in 2020 IEEE International Conference on Robotics and Automation (ICRA) . Institute of Electrical and Electronics Engineers (IEEE), pp. 3168-3174, 2020 International Conference on Robotics and Automation, Virtual conference, France, 31/05/20 . https://doi.org/10.1109/ICRA40945.2020.9197353
ICRA
ICRA
Deployment and operation of autonomous underwater vehicles is expensive and time-consuming. High-quality realistic sonar data simulation could be of benefit to multiple applications, including training of human operators for post-mission analysis, as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec740a0a7f578c297c64c186b04cb27b
http://arxiv.org/abs/1910.06750
http://arxiv.org/abs/1910.06750
Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a012d22fe69360258bcd11a1e5501b2f
https://doi.org/10.1109/icdl-epirob44920.2019
https://doi.org/10.1109/icdl-epirob44920.2019