Learned Initializations for Optimizing Coordinate-Based Neural Representations

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Overview

Learned Initializations for Optimizing Coordinate-Based Neural Representations

Project Page | Paper

Open Demo in Colab

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

Abstract

Teaser Image

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.

Code

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.

Owner
Matthew Tancik
Matthew Tancik
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