paper
A Systematic Approach for Selecting Trajectories for Data Augmentation
paperactiveprovisional
a-systematic-approach-for-selecting-trajectories-for-data-augmentation-2da3ea7f·1 events·first seen 7d agoAliases: A Systematic Approach for Selecting Trajectories for Data Augmentation
Co-occurring entities
More like this (12)
Variable-Speed Trajectory Augmentationcounterfactual data augmentationBehavioral Trajectory Tracking FrameworkProvenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curationchain-of-thought training data generationiterative trajectory refinementReference-Augmented TrainingHumanoid-GPT: Scaling Data and Structure for Zero-Shot Motion TrackingTextImage Augmentationstochastic gradient ascentSelf-Augmenting Retrieval for Diffusion Language ModelsProbe Trajectories
Recent events (1)
Systematic framework for selecting trajectories in data augmentation evaluated across five strategies
A thesis-derived arXiv preprint proposes a framework for evaluating five trajectory selection strategies—Outlierness, Diversity, Representativeness, Uncertainty, and Random—for data augmentation in spatio-temporal ML tasks. The study tests these strategies across four datasets spanning animal behavior, maritime, and urban traffic domains using linear and non-linear models with Optuna-based hyperparameter optimization. Key findings show systematic strategies (especially Outlierness and Uncertainty) outperform random selection in sparse datasets but can degrade performance in dense, high-quality datasets, with UMAP visualization confirming topological effects.