In Search of a Data Transformation That Accelerates Neural Field Training
This addresses a key bottleneck for widespread adoption of neural fields by improving training efficiency, though it is incremental as it focuses on a specific optimization aspect.
The paper tackles the slow training speed of neural fields by investigating how data transformations, specifically random pixel permutations, affect convergence. They found that randomly permuting pixel locations can considerably accelerate training, as it removes easy-to-fit patterns that hinder capturing fine details.
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.