  Coordinate-based neural representations have shown significant promise as an
alternative to discrete, array-based representations for complex low
dimensional signals. However, optimizing a coordinate-based network from
randomly initialized weights for each new signal is inefficient. We propose
applying standard meta-learning algorithms to learn the initial weight
parameters for these fully-connected networks based on the underlying class of
signals being represented (e.g., images of faces or 3D models of chairs).
Despite requiring only a minor change in implementation, using these learned
initial weights enables faster convergence during optimization and can serve as
a strong prior over the signal class being modeled, resulting in better
generalization when only partial observations of a given signal are available.
We explore these benefits across a variety of tasks, including representing 2D
images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D
image observations.
