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Found 2 papers, 2 papers with code
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation.
SR2CNN: Zero-Shot Learning for Signal Recognition
This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation.
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