Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Nathan Hubens"'
Autor:
Victor Delvigne, Noé Tits, Luca La Fisca, Nathan Hubens, Antoine Maiorca, Hazem Wannous, Thierry Dutoit, Jean-Philippe Vandeborre
Publikováno v:
Informatics, Vol 9, Iss 1, p 26 (2022)
Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the o
Externí odkaz:
https://doaj.org/article/0bd2e5ee06ca40e19823eefaa736d5b3
Publikováno v:
Electronics; Volume 11; Issue 22; Pages: 3789
FasterAI is a PyTorch-based library, aiming to facilitate the use of deep neural network compression techniques, such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick imple
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bc12644340998a8427381a7aec79bcb
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::044e2b97cd28ab06ad131a0cf5ab6bcc
https://doi.org/10.1109/icip46576.2022.9897980
https://doi.org/10.1109/icip46576.2022.9897980
Publikováno v:
Applications of Digital Image Processing XLIV.
Recent advances in video manipulation techniques have made synthetic media creation more accessible than ever before. Nowadays, video edition is so realistic that we cannot rely exclusively on our senses to assess the veracity of media content. With
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b724ef2dfdda3f73216d123d9bc9dc2e
https://doi.org/10.1117/12.2596317
https://doi.org/10.1117/12.2596317
An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network
Autor:
Matei Mancas, Bernard Gosselin, Titus Zaharia, Marc Decombas, Marius Preda, Nathan Hubens, Thierry Dutoit
Publikováno v:
APPIS
APPIS 2020: Proceedings of the 3rd International Conference on Applications of Intelligent Systems
APPIS 2020: 3rd International Conference on Applications of Intelligent Systems
APPIS 2020: 3rd International Conference on Applications of Intelligent Systems, Jan 2020, Las Palmas de Gran Canaria, Spain. pp.1-6, ⟨10.1145/3378184.3378224⟩
APPIS 2020: Proceedings of the 3rd International Conference on Applications of Intelligent Systems
APPIS 2020: 3rd International Conference on Applications of Intelligent Systems
APPIS 2020: 3rd International Conference on Applications of Intelligent Systems, Jan 2020, Las Palmas de Gran Canaria, Spain. pp.1-6, ⟨10.1145/3378184.3378224⟩
In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely hinders their deployment, notably on mobile devices or for real-time ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ef46939518799294494edf00624eb42
Publikováno v:
Electronics (2079-9292); Nov2022, Vol. 11 Issue 22, p3789, 19p
Autor:
Delvigne, Victor, Tits, Noé, La Fisca, Luca, Hubens, Nathan, Maiorca, Antoine, Wannous, Hazem, Dutoit, Thierry, Vandeborre, Jean-Philippe
Publikováno v:
Informatics; Mar2022, Vol. 9 Issue 1, p26-N.PAG, 15p
The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,The 168 papers included in
The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in the
Autor:
Arvind Kumar Tiwari
In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, etc. This book presents an introduction to deep lea