Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Yang, Shujun"'
Publikováno v:
journal={IEEE Geoscience and Remote Sensing Letters}, year={2023}, publisher={IEEE}
Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a group of candidate labels, amon
Externí odkaz:
http://arxiv.org/abs/2405.17110
Autor:
Bartoš, František, Sarafoglou, Alexandra, Godmann, Henrik R., Sahrani, Amir, Leunk, David Klein, Gui, Pierre Y., Voss, David, Ullah, Kaleem, Zoubek, Malte J., Nippold, Franziska, Aust, Frederik, Vieira, Felipe F., Islam, Chris-Gabriel, Zoubek, Anton J., Shabani, Sara, Petter, Jonas, Roos, Ingeborg B., Finnemann, Adam, Lob, Aaron B., Hoffstadt, Madlen F., Nak, Jason, de Ron, Jill, Derks, Koen, Huth, Karoline, Terpstra, Sjoerd, Bastelica, Thomas, Matetovici, Magda, Ott, Vincent L., Zetea, Andreea S., Karnbach, Katharina, Donzallaz, Michelle C., John, Arne, Moore, Roy M., Assion, Franziska, van Bork, Riet, Leidinger, Theresa E., Zhao, Xiaochang, Motaghi, Adrian Karami, Pan, Ting, Armstrong, Hannah, Peng, Tianqi, Bialas, Mara, Pang, Joyce Y. -C., Fu, Bohan, Yang, Shujun, Lin, Xiaoyi, Sleiffer, Dana, Bognar, Miklos, Aczel, Balazs, Wagenmakers, Eric-Jan
Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected $350{,}757$ coin flips to test the counterintuitive prediction from a physics model of human
Externí odkaz:
http://arxiv.org/abs/2310.04153
Federated Learning (FL) aims to learn a single global model that enables the central server to help the model training in local clients without accessing their local data. The key challenge of FL is the heterogeneity of local data in different client
Externí odkaz:
http://arxiv.org/abs/2304.01783
In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics, including the local spatial information and low-rankness. SP-DLRR is m
Externí odkaz:
http://arxiv.org/abs/2108.11172