REFINE: Trajectory Representation Learning via Closed-Loop Transcription -- Extended Version
Quick summary
arXiv:2609.07206v1 Announce Type: cross Abstract: Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware g
Key takeaways
- arXiv:2609.07206v1 Announce Type: cross Abstract: Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale.
- We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data.
- Drawing upon feedback control theory, REFINE tightly couples road-network-aware g
Why it matters
The importance of “REFINE: Trajectory Representation Learning via Closed-Loop Transcription -- Extended Version” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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