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

In Search of a Data Transformation That Accelerates Neural Field Training