首个系统优化隐式神经表示配置的框架，告别试错调参。
Beyond Heuristics: Globally Optimal Configuration of Implicit Neural Representations
- 将激活函数与初始化参数联合建模为可优化问题
- 在多类信号任务中实现性能提升20%以上
- 适合需要稳定高效隐式表示的研究者
隐式神经表示（INRs）在图像重建到3D建模等任务中表现优异，但其性能受限于缺乏系统化的配置策略，包括激活函数选择、初始化尺度、层间自适应及其复杂耦合关系。当前方法依赖启发式或暴力搜索，导致跨模态结果不一致。本文提出OptiINR，首个将INR配置转化为严格优化问题的统一框架，采用贝叶斯优化，协同搜索离散激活族（如SIREN、WIRE、FINER）及其连续初始化参数。该方法替代了碎片化的手动调参，实现了数据驱动的全局最优配置，在多种信号处理任务中持续提升性能。
原文摘要 · Abstract (English)
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.
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