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.
SelectAugment: Hierarchical deterministic sample selection for data augmentation,
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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.