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VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification

VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification

14 April 2025
Lucas Heublein
Simon Kocher
Tobias Feigl
A. Rügamer
Christopher Mutschler
Felix Ott
    DRL
ArXiv (abs)PDFHTML

Papers citing "VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification"

10 / 10 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
196
8
0
31 Mar 2025
GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning
Ali Ghanbarzade
Hossein Soleimani
AAML
73
1
0
04 Jan 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
109
7
0
31 Dec 2024
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
104
7
0
03 Oct 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
71
8
0
23 Sep 2024
Edge AI: A Taxonomy, Systematic Review and Future Directions
Edge AI: A Taxonomy, Systematic Review and Future Directions
S. Gill
Muhammed Golec
Jianmin Hu
Minxian Xu
Junhui Du
...
Kejiang Ye
Prabal Verma
Surendra Kumar
Félix Cuadrado
Steve Uhlig
80
30
0
04 Jul 2024
A Survey of Distributed Learning in Cloud, Mobile, and Edge Settings
A Survey of Distributed Learning in Cloud, Mobile, and Edge Settings
Madison Threadgill
A. Gerstlauer
76
1
0
23 May 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
111
9
0
17 May 2024
Communication-Efficient Large-Scale Distributed Deep Learning: A
  Comprehensive Survey
Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
Feng Liang
Zhen Zhang
Haifeng Lu
Victor C. M. Leung
Yanyi Guo
Xiping Hu
GNN
84
8
0
09 Apr 2024
Disentangling by Factorising
Disentangling by Factorising
Hyunjik Kim
A. Mnih
CoGeOOD
64
1,356
0
16 Feb 2018
1