From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Saratchandran, Hemanth Ramasinghe, Sameera Lucey, Simon |
| author_facet | Saratchandran, Hemanth Ramasinghe, Sameera Lucey, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Activation to Initialization: Scaling Insights for Optimizing Neural Fields Saratchandran, Hemanth Ramasinghe, Sameera Lucey, Simon Computer Vision and Pattern Recognition Machine Learning 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. |
| title | From Activation to Initialization: Scaling Insights for Optimizing Neural Fields |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.19205 |