Results 11 to 20 of about 738,625 (360)
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction [PDF]
Computer Vision and Pattern Recognition, 2022 Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity ...
Liang Gao +5 more
semanticscholar +1 more source
Federated Learning under Distributed Concept Drift [PDF]
International Conference on Artificial Intelligence and Statistics, 2022 Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for FL, because drifts arise staggered in time and space (across clients). To
Ellango Jothimurugesan +4 more
semanticscholar +1 more source
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams [PDF]
IEEE Internet of Things Magazine, 2021 In recent years, with the increasing popularity of “Smart Technology”, the number of Internet of Things (IoT) devices and systems have surged significantly.
Li Yang, A. Shami
semanticscholar +1 more source
Semantic Drift Compensation for Class-Incremental Learning [PDF]
Computer Vision and Pattern Recognition, 2020 Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes.
Lu Yu +7 more
semanticscholar +1 more source
Data drift in medical machine learning: implications and potential remedies.
British Journal of Radiology, 2023 Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation.
B. Sahiner +3 more
semanticscholar +1 more source
Learning under Concept Drift: A Review [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2019 Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation.
Jie Lu +5 more
semanticscholar +1 more source
Point Set Registration: Coherent Point Drift [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009 Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other.
Andriy Myronenko, Xubo B. Song
semanticscholar +1 more source
Characteristics of unmanned aerial spraying systems and related spray drift: A review
Frontiers in Plant Science, 2022 Although drift is not a new issue, it deserves further attention for Unmanned Aerial Spraying Systems (UASS). The use of UASS as a spraying tool for Plant Protection Products is currently explored and applied worldwide. They boast different benefits such
Pengchao Chen +6 more
semanticscholar +1 more source
A Survey on Concept Drift in Process Mining [PDF]
ACM Computing Surveys, 2021 Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version.
Denise Maria Vecino Sato +3 more
semanticscholar +1 more source
Do Arctic waders use adaptive wind drift? [PDF]
, 2004 We analysed five data sets of flight directions of migrating arctic waders in relation to winds, recorded by tracking radar and optical range finder, in order to find out if these birds compensate for wind drift, or allow themselves to be drifted by ...
Hedenström, Anders, +20 more
core +1 more source