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TIML: Task-Informed Meta-Learning for Agriculture
[article]
2022
arXiv
pre-print
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
arXiv:2202.02124v1
fatcat:3mtitlt6srcehiretruhug6fwu