In Search of a Data Transformation That Accelerates Neural Field Training

TL;DR

This paper investigates how data transformations, specifically random pixel permutations, can significantly accelerate neural field training by altering the optimization landscape and error patterns.

Contribution

It reveals that random pixel permutations improve training speed by disrupting easy-to-fit patterns, a novel insight into neural field optimization.

Findings

Random pixel permutations accelerate training convergence.

Permutations remove easy-to-fit patterns that hinder fine detail learning.

Analysis of loss landscapes explains the acceleration phenomenon.

Abstract

Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal. A key obstacle that prevents its widespread adoption is the encoding speed-generating neural fields requires an overfitting of a neural network, which can take a significant number of SGD steps to reach the desired fidelity level. In this paper, we delve into the impacts of data transformations on the speed of neural field training, specifically focusing on how permuting pixel locations affect the convergence speed of SGD. Counterintuitively, we find that randomly permuting the pixel locations can considerably accelerate the training. To explain this phenomenon, we examine the neural field training through the lens of PSNR curves, loss landscapes, and error patterns. Our analyses suggest that the random pixel permutations remove the easy-to-fit patterns, which facilitate easy optimization in the early stage but hinder capturing fine details of the signal.