Subtractive Modulative Network with Learnable Periodic Activations

• URL: http://arxiv.org/abs/2602.16337v1
• Date: Wed, 18 Feb 2026 10:20:50 GMT
• Title: Subtractive Modulative Network with Learnable Periodic Activations
• Authors: Tiou Wang, Zhuoqian Yang, Markus Flierl, Mathieu Salzmann, Sabine Süsstrunk,
• Abstract summary: We propose a novel, parameter-efficient Implicit Neural Representation architecture inspired by classical subtractive synthesis.<n>Our SMN achieves a PSNR of $40+$ dB on two image datasets, comparing favorably against state-of-the-art methods in terms of both reconstruction accuracy and parameter efficiency.
• Score: 59.89799070130572
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose the Subtractive Modulative Network (SMN), a novel, parameter-efficient Implicit Neural Representation (INR) architecture inspired by classical subtractive synthesis. The SMN is designed as a principled signal processing pipeline, featuring a learnable periodic activation layer (Oscillator) that generates a multi-frequency basis, and a series of modulative mask modules (Filters) that actively generate high-order harmonics. We provide both theoretical analysis and empirical validation for our design. Our SMN achieves a PSNR of $40+$ dB on two image datasets, comparing favorably against state-of-the-art methods in terms of both reconstruction accuracy and parameter efficiency. Furthermore, consistent advantage is observed on the challenging 3D NeRF novel view synthesis task. Supplementary materials are available at https://inrainbws.github.io/smn/.

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