Matthew Tancik*¹, Ben Mildenhall*¹, Terrance Wang¹, Divi Schmidt¹, Pratul P. Srinivasan², Jonathan T. Barron², Ren Ng¹

¹UC Berkeley, ²Google Research ^*denotes equal contribution

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 provide a demo IPython notebook as a simple reference for the core idea. Scripts for the different tasks are located in the Experiments directory.