From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
Hemanth Saratchandran, Sameera Ramasinghe, Simon Lucey
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 a deep-seated connection among network initialization architectural choices and the optimization process emphasizing the need for a holistic approach when designing cutting-edge Neural Fields.
BibTeX
@inproceedings{cvpr2024_fromactivationto,
title = {From Activation to Initialization: Scaling Insights for Optimizing Neural Fields},
author = {Hemanth Saratchandran and Sameera Ramasinghe and Simon Lucey},
booktitle = {CVPR 2024},
year = {2024}
}