Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data
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
Code (0)
등록된 구현이 없습니다.
Tasks
Few-Shot LearningSimilar Papers 제목 키워드 기반
Quadruplet Selection Methods for Deep Embedding Learning
Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we …
feature selectionMulti-Task LearningCo-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data. However,the acquisition of a large supervised dataset is often challenging. This is also true for intelligent …
General ClassificationTraffic Sign RecognitionAn Efficient Framework for Zero-Shot Sketch-Based Image Retrieval
Recently, Zero-shot Sketch-based Image Retrieval (ZS-SBIR) has attracted the attention of the computer vision community due to it's real-world applications, and the more realistic and challenging setting than found in SB…
Content-Based Image RetrievalDomain AdaptationImage Retrievalobject-detection+4Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification
An ugly duckling is an obviously different skin lesion from surrounding lesions of an individual, and the ugly duckling sign is a criterion used to aid in the diagnosis of cutaneous melanoma by differentiating between hi…
Lesion ClassificationMetric LearningSkin Lesion ClassificationTripletImproving Answer Selection and Answer Triggering using Hard Negatives
In this paper, we establish the effectiveness of using hard negatives, coupled with a siamese network and a suitable loss function, for the tasks of answer selection and answer triggering. We show that the choice of samp…
Answer Selection