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Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling

Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling

Lucas Heublein
Tobias Feigl
A. Rügamer
Felix Ott
ArXiv (abs)PDFHTML

Papers citing "Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling"

8 / 8 papers shown
Title
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
Lucas Heublein
Nisha Lakshmana Raichur
Tobias Feigl
Tobias Brieger
Fin Heuer
Lennart Asbach
A. Rügamer
Felix Ott
202
8
0
31 Mar 2025
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Nishant S. Gaikwad
Lucas Heublein
Nisha Lakshmana Raichur
Tobias Feigl
Christopher Mutschler
Felix Ott
111
7
0
31 Dec 2024
Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
Lucas Heublein
Tobias Feigl
Thorsten Nowak
A. Rügamer
Christopher Mutschler
Felix Ott
78
8
0
23 Sep 2024
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
Nisha Lakshmana Raichur
Lucas Heublein
Tobias Feigl
A. Rügamer
Christopher Mutschler
Felix Ott
CLLBDL
115
9
0
17 May 2024
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints
Zi Wang
Minglin Chen
Yulan Guo
Zhang Li
Qifeng Yu
64
33
0
23 Dec 2022
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve
Kevin Duarte
Yogesh S Rawat
M. Shah
320
521
0
15 Jan 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
847
5,847
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCVBDL
891
9,364
0
06 Jun 2015