Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
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DOI：
10.48550/arxiv.2212.06864
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Zhexiong Liu;Licheng Liu;Yiqun Xie;Zhenong Jin;X. Jia
中科院分区：
文献类型：
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作者：
Zhexiong Liu;Licheng Liu;Yiqun Xie;Zhenong Jin;X. Jia
时空机器学习对于农业监测、水文预报和交通管理等各种社会应用都是至关重要的。这些应用在很大程度上依赖于表征空间和时间差异的区域特征。然而，时空数据在不同地点往往表现出复杂的模式和显著的数据变异性。在许多现实世界的应用中，标签也可能是有限的，这使得很难为不同的位置单独训练独立的模型。尽管元学习在小样本的模型适应方面显示出前景，但现有的元学习方法在处理大量异构任务方面仍然有限，例如，具有不同数据模式的大量位置。为了弥补这一差距，我们提出了任务自适应公式和一个模型无关的元学习框架，该框架将区域异构数据转换为位置敏感的元任务。我们遵循易难任务层次进行任务适应，其中不同的元模型适应不同难度的任务。该方法的一个主要优点是提高了模型对大量异构任务的适应能力。它还通过自动调整相应难度的元模型来适应任何新的任务，从而增强了模型的泛化能力。我们证明了我们提出的框架优于各种基线和最先进的元学习框架。我们对实际作物产量数据的大量实验表明，所提出的方法在处理实际社会应用中与空间相关的异构任务方面是有效的。
Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.