FM-SIREN & FM-FINER: Implicit Neural Representation Using Nyquist-based Orthogonality
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913023321440256 |
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| author | Alsakabi, Mohammed Mobeirek, Wael Dolan, John M. Tonguz, Ozan K. |
| author_facet | Alsakabi, Mohammed Mobeirek, Wael Dolan, John M. Tonguz, Ozan K. |
| contents | Existing periodic activation-based implicit neural representation (INR) networks, such as SIREN and FINER, suffer from hidden feature redundancy, where neurons within a layer capture overlapping frequency components due to the use of a fixed frequency multiplier. This redundancy limits the expressive capacity of multilayer perceptrons (MLPs). Drawing inspiration from classical signal processing methods such as the Discrete Sine Transform (DST), in this paper, we propose FM-SIREN and FM-FINER, which assign Nyquist-informed, neuron-specific frequency multipliers to periodic activations. Contrary to existing approaches, our design introduces frequency diversity without requiring hyperparameter tuning or additional network depth. This simple yet principled approach reduces the redundancy of features by nearly 50% and consistently improves signal reconstruction across diverse INR tasks, such as fitting 1D audio, 2D image and 3D shape, and video, outperforming their baseline counterparts while maintaining efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23438 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FM-SIREN & FM-FINER: Implicit Neural Representation Using Nyquist-based Orthogonality Alsakabi, Mohammed Mobeirek, Wael Dolan, John M. Tonguz, Ozan K. Computer Vision and Pattern Recognition Existing periodic activation-based implicit neural representation (INR) networks, such as SIREN and FINER, suffer from hidden feature redundancy, where neurons within a layer capture overlapping frequency components due to the use of a fixed frequency multiplier. This redundancy limits the expressive capacity of multilayer perceptrons (MLPs). Drawing inspiration from classical signal processing methods such as the Discrete Sine Transform (DST), in this paper, we propose FM-SIREN and FM-FINER, which assign Nyquist-informed, neuron-specific frequency multipliers to periodic activations. Contrary to existing approaches, our design introduces frequency diversity without requiring hyperparameter tuning or additional network depth. This simple yet principled approach reduces the redundancy of features by nearly 50% and consistently improves signal reconstruction across diverse INR tasks, such as fitting 1D audio, 2D image and 3D shape, and video, outperforming their baseline counterparts while maintaining efficiency. |
| title | FM-SIREN & FM-FINER: Implicit Neural Representation Using Nyquist-based Orthogonality |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.23438 |