In Search of a Data Transformation That Accelerates Neural Field Training

arXiv:2311.17094v2 Announce Type: replace
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 …

abstract adoption arxiv cs.cv cs.lg data data transformation encoding fields key network neural network overfitting paradigm representation search signal speed training trains transformation type