Beyond Heuristics: Globally Optimal Configuration of Implicit Neural Representations

2025-09-27

AI Summary

Implicit neural representations (INRs) suffer from suboptimal performance due to the absence of systematic, joint design principles for activation functions and initialization parameters; existing approaches rely on heuristic tuning or exhaustive search, yielding inconsistent results across modalities. Method: We propose the first unified joint optimization framework for INR configuration, modeling discrete activation families (e.g., SIREN, WIRE, FINER) and continuous initialization scales as co-optimized variables, and employing Bayesian optimization for end-to-end automated configuration. Contribution/Results: Our method replaces ad hoc empirical practices with a data-driven, standardized configuration pipeline. Evaluated on multimodal signal reconstruction and 3D shape modeling tasks, it achieves significant improvements in reconstruction accuracy and training stability, demonstrating strong generalizability and cross-modal consistency.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Implicit Neural Representations (INRs) have emerged as a transformative paradigm in signal processing and computer vision, excelling in tasks from image reconstruction to 3D shape modeling. Yet their effectiveness is fundamentally limited by the absence of principled strategies for optimal configuration - spanning activation selection, initialization scales, layer-wise adaptation, and their intricate interdependencies. These choices dictate performance, stability, and generalization, but current practice relies on ad-hoc heuristics, brute-force grid searches, or task-specific tuning, often leading to inconsistent results across modalities. This work introduces OptiINR, the first unified framework that formulates INR configuration as a rigorous optimization problem. Leveraging Bayesian optimization, OptiINR efficiently explores the joint space of discrete activation families - such as sinusoidal (SIREN), wavelet-based (WIRE), and variable-periodic (FINER) - and their associated continuous initialization parameters. This systematic approach replaces fragmented manual tuning with a coherent, data-driven optimization process. By delivering globally optimal configurations, OptiINR establishes a principled foundation for INR design, consistently maximizing performance across diverse signal processing applications.

Problem

• Optimizing activation selection and initialization for neural representations
• Replacing heuristic tuning with unified Bayesian optimization framework
• Finding globally optimal configurations across diverse signal processing tasks

Innovation

• Unified framework formulates INR configuration as optimization
• Bayesian optimization explores joint discrete and continuous parameter space
• Systematic approach replaces manual tuning with data-driven optimization