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Learning Across Domains and Devices: Style-Driven Source-Fr..:

, In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),

Shenaj, Donald ; Fani, Eros ; Toldo, Marco... - p. 444-454 , 2023

Link: https://doi.org/10.1109/WACV56688.2023.00052

RT 
T1 <cTitle>2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)</cTitle>: T1 <Title>Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10030428&Exemplar=1&LAN=DE
				
A1 Shenaj, Donald
A1 Fani, Eros
A1 Toldo, Marco
A1 Caldarola, Debora
A1 Tavera, Antonio
A1 Michieli, Umberto
A1 Ciccone, Marco
A1 Zanuttigh, Pietro
A1 Caputo, Barbara
YR 2023
SN 2642-9381
K1 Training
K1 Computer vision
K1 Adaptation models
K1 Codes
K1 Federated learning
K1 Semantic segmentation
K1 Clustering algorithms
K1 Algorithms: Image recognition and understanding (object detection
K1 categorization
K1 segmentation)
K1 Machine learning architectures
K1 formulations
K1 and algorithms (including transfer
K1 low-shot
K1 semi-
K1 self-
K1 and un-supervised learning)
SP 444
OP 454
LK http://dx.doi.org/https://doi.org/10.1109/WACV56688.2023.00052
DO https://doi.org/10.1109/WACV56688.2023.00052
SF ELIB - SuUB Bremen

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