Empirical comparison of Outlierness, Diversity, Representativeness, Uncertainty, and Random selection for trajectory data augmentation across four datasets shows conditional gains in stability over random baselines but degradation in dense data.
Deep learning for trajectory classification: A survey,
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A Systematic Approach for Selecting Trajectories for Data Augmentation
Empirical comparison of Outlierness, Diversity, Representativeness, Uncertainty, and Random selection for trajectory data augmentation across four datasets shows conditional gains in stability over random baselines but degradation in dense data.