JA-SIREN: Deterministic Initialization for Sinusoidal Networks via Spectral Matching

2026-06-04 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

JA-SIREN (Jacobi-Anger Sinusoidal Representation Network) introduces a deterministic initialization scheme for sinusoidal networks, directly addressing the significant performance inconsistencies observed in existing implicit neural representation (INR) approaches. Current stochastic initialization methods can lead to variations exceeding 2.5 dB (78%) in image regression, posing a critical challenge for scientific computing and simulation where reproducibility is paramount. JA-SIREN overcomes this by leveraging classical spectral analysis, specifically computing the Discrete Sine Transform (DST) of the target signal and employing the Jacobi-Anger expansion. This process derives closed-form weights for a two-layer sinusoidal MLP, analytically matching the network's initial spectral response to the target signal without requiring random seeds or hyperparameter tuning. On the Kodak dataset, JA-SIREN achieved a mean PSNR of 67.18 dB, representing a 21.30 dB improvement over the best baseline, with zero run-to-run variance.

Key takeaway

For Machine Learning Engineers developing implicit neural representations (INRs) where consistent and high-quality results are crucial, consider adopting spectrally-informed initialization techniques. Stochastic initialization introduces unacceptable run-to-run variance, impacting reproducibility in applications like image regression and scientific simulation. Implementing a deterministic scheme like JA-SIREN, which analytically matches network spectral response, can yield substantial performance gains, such as a 21.30 dB PSNR improvement, and eliminate variance, ensuring reliable model deployment.

Key insights

Deterministic initialization via spectral matching for sinusoidal networks significantly enhances INR reproducibility and performance.

Principles

• Spectrally-informed initialization is more effective
• Deterministic initialization ensures reproducibility

Method

Compute the Discrete Sine Transform (DST) of the target signal, then use Jacobi-Anger expansion to derive closed-form weights for a two-layer sinusoidal MLP.

In practice

• Apply to implicit neural representation (INR) tasks
• Improve image regression consistency
• Enhance scientific computing simulations

Topics

• JA-SIREN
• Implicit Neural Representations
• Sinusoidal Networks
• Deterministic Initialization
• Spectral Analysis
• Image Regression
• Reproducibility

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.