  We propose a few-shot learning method for spatial regression. Although
Gaussian processes (GPs) have been successfully used for spatial regression,
they require many observations in the target task to achieve a high predictive
performance. Our model is trained using spatial datasets on various attributes
in various regions, and predicts values on unseen attributes in unseen regions
given a few observed data. With our model, a task representation is inferred
from given small data using a neural network. Then, spatial values are
predicted by neural networks with a GP framework, in which task-specific
properties are controlled by the task representations. The GP framework allows
us to analytically obtain predictions that are adapted to small data. By using
the adapted predictions in the objective function, we can train our model
efficiently and effectively so that the test predictive performance improves
when adapted to newly given small data. In our experiments, we demonstrate that
the proposed method achieves better predictive performance than existing
meta-learning methods using spatial datasets.
