# Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data

arXiv: https://arxiv.org/abs/2402.09466 · HTML: https://mlsanity.com/paper/2402.09466/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Prototype Rectification for Few-Shot Learning](https://mlsanity.com/paper/1911.10713/) (2019)
- [Laplacian Regularized Few-Shot Learning](https://mlsanity.com/paper/2006.15486/) (2020)
- [Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification](https://mlsanity.com/paper/2012.08733/) (2020)
- [A Closer Look at Few-shot Classification Again](https://mlsanity.com/paper/2301.12246/) (2023)
- [Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval](https://mlsanity.com/paper/2011.12663/) (2020)
- [A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning](https://mlsanity.com/paper/1904.08720/) (2019)
- [Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift](https://mlsanity.com/paper/2204.03342/) (2022)
- [Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift](https://mlsanity.com/paper/2206.08640/) (2022)
- [Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition](https://mlsanity.com/paper/2202.07901/) (2022)
- [Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning](https://mlsanity.com/paper/2106.00638/) (2021)

## Built on by (most cited first, 12 of 12)

- [Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning](https://mlsanity.com/paper/2405.11067/) (2024)
- [Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization](https://mlsanity.com/paper/2409.15114/) (2024)
- [Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification](https://mlsanity.com/paper/2410.15681/) (2024)
- [Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling](https://mlsanity.com/paper/2410.14686/) (2024)
- [Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies](https://mlsanity.com/paper/2503.23775/) (2025)
- [Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization](https://mlsanity.com/paper/2501.05079/) (2025)
- [Attention-Based Fusion of IQ and FFT Spectrograms with AoA Features for GNSS Jammer Localization](https://mlsanity.com/paper/2507.14167/) (2025)
- [VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification](https://mlsanity.com/paper/2504.10556/) (2025)
- [GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System](https://mlsanity.com/paper/2512.05128/) (2025)
- [GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU](https://mlsanity.com/paper/2605.14839/) (2026)
- [The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding](https://mlsanity.com/paper/2607.03411/) (2026)
- [SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition](https://mlsanity.com/paper/2609.34875/) (2026)

## Abstract (arXiv)

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. In this paper, we propose a few-shot learning (FSL) approach to adapt to new interference classes. Our method employs quadruplet selection for the model to learn representations using various positive and negative interference classes. Furthermore, our quadruplet variant selects pairs based on the aleatoric and epistemic uncertainty to differentiate between similar classes. We recorded a dataset at a motorway with eight interference classes on which our FSL method with quadruplet loss outperforms other FSL techniques in jammer classification accuracy with 97.66%. Dataset available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway