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- W4386825278 abstract "Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution: <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>S</u> patial- <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>T</u> emporal <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>C</u> ontrasting for Fine-Grained Urban <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>F</u> low Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attracting <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>spatial-temporally similar</i> flow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts." @default.
- W4386825278 created "2023-09-19" @default.
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- W4386825278 date "2023-01-01" @default.
- W4386825278 modified "2023-10-01" @default.
- W4386825278 title "Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference" @default.
- W4386825278 doi "https://doi.org/10.1109/tbdata.2023.3316471" @default.
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