Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Chaithanya Kumar Mummadi"'
Autor:
Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta, Mauricio Munoz, Volker Fischer
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031167874
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55e056b5a9ec9a4a1ba046824b21f042
https://doi.org/10.1007/978-3-031-16788-1_7
https://doi.org/10.1007/978-3-031-16788-1_7
Autor:
Chaithanya Kumar Mummadi, Frederic Philips Peter Leo, Keshav Deep Verma, Shivaji Kasireddy, Philipp M. Scholl, Jochen Kempfle, Kristof Van Laerhoven
Publikováno v:
Informatics, Vol 5, Iss 2, p 28 (2018)
This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by usin
Externí odkaz:
https://doaj.org/article/d24cfc993ed343c5b34feabc328844ea
Autor:
Elias Eulig, Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Kilian Rambach, William Beluch, Xiahan Shi, Volker Fischer
Common deep neural networks (DNNs) for image classification have been shown to rely on shortcut opportunities (SO) in the form of predictive and easy-to-represent visual factors. This is known as shortcut learning and leads to impaired generalization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4e329f78df7715c7847eeb6596dcfd8
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030336752
GCPR
GCPR
Deep neural networks achieve state-of-the-art results on several tasks while increasing in complexity. It has been shown that neural networks can be pruned during training by imposing sparsity inducing regularizers. In this paper, we investigate two
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7b10928dbae4b87a7f7129c094a528a
https://doi.org/10.1007/978-3-030-33676-9_10
https://doi.org/10.1007/978-3-030-33676-9_10
Publikováno v:
ICCV
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of image classifiers against such adversaria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f7233c95bc5e130f825a311caab4af6
http://arxiv.org/abs/1812.03705
http://arxiv.org/abs/1812.03705
Autor:
Frederic Philips Peter Leo, Kristof Van Laerhoven, Jochen Kempfle, Philipp M. Scholl, Shivaji Kasireddy, Chaithanya Kumar Mummadi, Keshav Deep Verma
Publikováno v:
Informatics
Volume 5
Issue 2
Informatics, Vol 5, Iss 2, p 28 (2018)
Volume 5
Issue 2
Informatics, Vol 5, Iss 2, p 28 (2018)
This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by usin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::017567259988e4112733323516b67e8d
Autor:
Shivaji Kasireddy, Keshav Deep Verma, Frederic Philips Peter Leo, Chaithanya Kumar Mummadi, Philipp M. Scholl, Kristof Van Laerhoven
Publikováno v:
iWOAR
Data gloves have numerous applications, including enabling novel human-computer interaction and automated recognition of large sets of gestures, such as those used for sign language. For most of these applications, it is important to build mobile and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e62a078a437a5b374c13a14e066e14f
https://doi.org/10.1145/3134230.3134236
https://doi.org/10.1145/3134230.3134236
Autor:
Wan, Zhijing1 (AUTHOR) wanzjwhu@whu.edu.cn, Wang, Zhixiang2 (AUTHOR) wangzx1994@gmail.com, Chung, Cheukting1 (AUTHOR) 2271406579@qq.com, Wang, Zheng1 (AUTHOR) wangzwhu@whu.edu.cn
Publikováno v:
ACM Computing Surveys. Jul2024, Vol. 56 Issue 7, p1-34. 34p.
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Feb2024, Vol. 18 Issue 2, p1-16, 16p
Publikováno v:
Cybernetics & Systems; 2023, Vol. 54 Issue 5, p604-618, 15p