Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Tristan Hascoet"'
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2023, Iss 1, Pp 1-30 (2023)
Abstract Training Convolutional Neural Networks (CNN) is a resource-intensive task that requires specialized hardware for efficient computation. One of the most limiting bottlenecks of CNN training is the memory cost associated with storing the activ
Externí odkaz:
https://doaj.org/article/c22705eb6df54be494103044d0fa92b6
Publikováno v:
Remote Sensing, Vol 16, Iss 1, p 170 (2023)
Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a
Externí odkaz:
https://doaj.org/article/5a52db3f98d94eb9873c30b8aa47a3e7
Publikováno v:
APSIPA Transactions on Signal and Information Processing, Vol 12, Iss 1 (2023)
Externí odkaz:
https://doaj.org/article/263c0415235144f4a2ac61bc48b1c317
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2019, Iss 1, Pp 1-14 (2019)
Abstract Zero-shot learning (ZSL) models use semantic representations of visual classes to transfer the knowledge learned from a set of training classes to a set of unknown test classes. In the context of generic object recognition, previous research
Externí odkaz:
https://doaj.org/article/2039d28880d94117ada3584f6e844526
Autor:
Tristan, Hascoet, Zhang, Yihao, Andreas, Persch, Takashima, Ryoichi, Takiguchi, Tetsuya, Ariki, Yasuo
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safe
Externí odkaz:
http://arxiv.org/abs/2010.11780
Autor:
Yasuo Ariki, Tetsuya Takiguchi, Ryoichi Takashima, Xunquan Chen, Tristan Hascoet, Weihao Zhuang
Publikováno v:
APSIPA Transactions on Signal and Information Processing. 12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28a42fa065195fd0f3d377d18c79729a
https://doi.org/10.1561/116.00000015
https://doi.org/10.1561/116.00000015
Autor:
Tetsuya Takiguchi, Tristan Hascoet, Xunquan Chen, Ryoichi Takashima, Weihao Zhuang, Yasuo Ariki
Currently, deep learning plays an indispensable role in many fields, including computer vision, natural language processing, and speech recognition. Convolutional Neural Networks (CNNs) have demonstrated excellent performance in computer vision tasks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe4b7e708a12728bb75992ded3e2bfe7
https://doi.org/10.21203/rs.3.rs-743636/v1
https://doi.org/10.21203/rs.3.rs-743636/v1
Publikováno v:
Journal of Software Engineering and Applications. 12:307-320
In recent years, Convolutional Neural Networks (CNNs) have enabled unprecedented progress on a wide range of computer vision tasks. However, training large CNNs is a resource-intensive task that requires specialized Graphical Processing Units (GPU) a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49d40fde1a8a837779cec00f842132f3
https://doi.org/10.4236/jsea.2019.128019
https://doi.org/10.4236/jsea.2019.128019
Autor:
Yasuo Ariki, Yihao Zhang, Tristan Hascoet, Tetsuya Takiguchi, Ryoichi Takashima, Andreas Persch
Publikováno v:
IEEE BigData
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9f616913600175bbbf45a49ff195108
https://doi.org/10.1109/bigdata50022.2020.9377871
https://doi.org/10.1109/bigdata50022.2020.9377871
Publikováno v:
GCCE
In this paper, we propose a method to reduce the memory requirement of Convolutional Neural Networks (CNN) in the inference phase. Before feeding an input image into the CNN model, input image will split evenly to several sub-images and feed them int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15c90b390a08d6c19befb06aa1d01af6
https://doi.org/10.1109/gcce50665.2020.9291891
https://doi.org/10.1109/gcce50665.2020.9291891