From Activation to Initialization: Scaling Insights for Optimizing Neural Fields

arXiv:2403.19205v1 Announce Type: cross
Abstract: In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field still lacks a comprehensive theoretical framework. This article aims to address this gap by delving into the intricate interplay between initialization and activation, providing a foundational basis for the robust optimization of Neural Fields. Our theoretical insights reveal …

abstract article arxiv computer computer vision cs.cv cs.lg fields framework insights networks neural networks progress representation scaling signal solve tool type vision