From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
This work addresses a foundational gap in computer vision for researchers developing Neural Fields, though it appears incremental as it builds on existing concepts without introducing a new paradigm.
The paper tackles the lack of a theoretical framework for optimizing Neural Fields by analyzing the interplay between initialization and activation, providing foundational insights for robust design.
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.