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.
Trajec- tory augmentation for robust neural locomotion controllers,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.