ComS2T: A Complementary Spatiotemporal Learning System for Data-Adaptive Model Evolution.

Zhengyang Zhou, Qihe Huang, Binwu Wang, Jianpeng Hou, Kuo Yang, Yuxuan Liang, Yu Zheng, Yang Wang
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Abstract

Spatiotemporal (ST) learning has become a crucial technique to enable smart cities and sustainable urban development. Current ST learning models capture the heterogeneity via various spatial convolution and temporal evolution blocks. However, rapid urbanization leads to fluctuating distributions in urban data and city structures, resulting in existing methods suffering generalization and data adaptation issues. Despite efforts, existing methods fail to deal with newly arrived observations, and the limitation of those methods with generalization capacity lies in the repeated training that leads to inconvenience, inefficiency and resource waste. Motivated by complementary learning in neuroscience, we introduce a prompt-based complementary spatiotemporal learning termed ComS2T, to empower the evolution of models for data adaptation. We first disentangle the neural architecture into two disjoint structures, a stable neocortex for consolidating historical memory, and a dynamic hippocampus for new knowledge update. Then we train the dynamic spatial and temporal prompts by characterizing distribution of main observations to enable prompts adaptive to new data. This data-adaptive prompt mechanism, combined with a two-stage training process, facilitates fine-tuning of the neural architecture conditioned on prompts, thereby enabling efficient adaptation during testing. Extensive experiments validate the efficacy of ComS2T in adapting various spatiotemporal out-of-distribution scenarios while maintaining effective inferences. The code is available on https://github.com/hqh0728/ComS2T.

ComS2T:一种数据自适应模型演化的互补时空学习系统。
时空学习已成为实现智慧城市和可持续城市发展的关键技术。目前的ST学习模型通过不同的空间卷积和时间进化块来捕获异质性。然而,快速城市化导致城市数据和城市结构的波动分布,导致现有方法存在泛化和数据适应问题。现有方法虽有努力,但无法处理新到达的观测值,具有泛化能力的方法的局限性在于反复训练,造成了不方便、低效率和资源浪费。受神经科学中互补学习的启发,我们引入了一种基于提示的互补时空学习,称为ComS2T,以增强数据适应模型的进化。我们首先将神经结构分解为两个不相连的结构,一个稳定的新皮层用于巩固历史记忆,一个动态的海马体用于更新新知识。然后,我们通过描述主要观测值的分布特征来训练动态时空提示,使提示能够适应新数据。这种数据自适应提示机制与两阶段训练过程相结合,有助于根据提示对神经结构进行微调,从而在测试期间实现有效的适应。大量的实验验证了ComS2T在适应各种时空非分布场景的同时保持有效推断的有效性。代码可在https://github.com/hqh0728/ComS2T上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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