TIML：面向农业的基于任务的元学习
TIML: Task-Informed Meta-Learning for Agriculture
摘要：
农业的标记数据集在空间上极不平衡。在为数据稀疏区域开发算法时，一种自然的方法是使用来自数据丰富区域的迁移学习。虽然标准的迁移学习方法通常只利用直接输入和输出，但地理空间图像和农业数据富含元数据，可以为迁移学习算法提供信息，例如数据点的空间坐标或正在学习的任务类别。我们建立在以前的工作，探索使用元学习的农业背景下，在数据稀疏的地区，并引入任务知情的元学习（TIML），增强模型不可知的元学习，它利用特定于任务的元数据。我们将TIML应用于作物类型分类和产量估计，并发现TIML在这两种情况下，在不同的模型架构中，与一系列基准相比，显着提高了性能。虽然我们专注于农业任务，但TIML可以为任何具有特定任务元数据的元学习设置提供好处，例如地理标记图像的分类和物种分布建模。
英文摘要：
Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data are rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of data-points or the class of task being learned. We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of task-specific metadata. We apply TIML to crop type classification and yield estimation, and find that TIML significantly improves performance compared to a range of benchmarks in both contexts, across a diversity of model architectures. While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling.
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