Work

Subtractive Modulative Network with Learnable Periodic Activations

Year: 2026

Type: preprint

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/.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2602.16337

Authors: Wang, Tiou, Yang, Zhuoqian, Flierl, Markus, Salzmann, Mathieu, Süsstrunk, Sabine

Institutions: KTH Royal Institute of Technology, École Polytechnique Fédérale de Lausanne

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7130575272

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