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Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
The primary objective of methods in continual learning is to learn tasks in a sequential manner over time from a stream of data, while mitigating the detrimental phenomenon of catastrophic forgetting.
Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data
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%.
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