From Activation to Initialization: Scaling Insights for Optimizing Neural Fields

Hemanth Saratchandran; Sameera Ramasinghe; Simon Lucey

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arXiv · 2403.19205

From Activation to Initialization: Scaling Insights for Optimizing Neural Fields

Hemanth Saratchandran·Sameera Ramasinghe·Simon Lucey

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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.

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BibTeX RIS

Hemanth Saratchandran, Sameera Ramasinghe, Simon Lucey. 2024-03-28. From Activation to Initialization: Scaling Insights for Optimizing Neural Fields. https://arxiv.org/abs/2403.19205

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