In Search of a Data Transformation That Accelerates Neural Field Training

Junwon Seo, Sangyoon Lee, Kwang In Kim, Jaeho Lee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4830-4839

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.