发表机构
Brookhaven National Laboratory（布鲁克海文国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对隐式神经表示训练中均匀采样效率低的问题，提出ACES结构化采样框架，通过自适应空间划分确保覆盖、区域级重要性加权聚焦信息丰富区域，实现更快收敛与更低误差。
AI 中文摘要
隐式神经表示（INRs）为建模高维连续场提供了一个灵活的框架，但其训练往往因忽略空间异质性的均匀子采样而效率低下。现有的自适应采样方法通过优先处理高误差样本来部分解决这一问题，但通常是在点级别操作，往往导致局部区域的冗余采样和域覆盖不足。我们提出了ACES（自适应覆盖感知的高效采样），一种结构化采样框架，通过解耦覆盖与重要性来提高训练效率。ACES构建自适应空间划分以确保域覆盖并减少冗余，并在训练过程中应用区域级重要性加权以优先处理信息丰富的区域。我们提供理论分析表明，自适应划分通过增加区域内同质性来降低梯度方差，并且区域级加权中的受控偏差可能相对于标准无偏估计器提高优化效率。在科学场学习任务上的实验表明，ACES比均匀和点级自适应采样基线实现更快的收敛和更低的误差，在具有高度局部化复杂性的场中收益最大。
英文摘要
Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampling that ignores spatial heterogeneity. Existing adaptive sampling methods partially address this issue by prioritizing high-error samples, but typically operate at the point level, often leading to redundant sampling in localized regions and insufficient coverage of the domain. We propose ACES (Adaptive Coverage-aware Efficient Sampling), a structured sampling framework that improves training efficiency by decoupling coverage and importance. ACES constructs adaptive spatial partitions to ensure domain coverage and reduce redundancy, and applies region-level importance weighting to prioritize informative regions during training. We provide a theoretical analysis showing that adaptive partitioning reduces gradient variance by increasing within-region homogeneity, and that controlled bias in region-level weighting may improve optimization efficiency relative to standard unbiased estimators. Experiments on scientific field learning tasks demonstrate that ACES achieves faster convergence and lower error than uniform and pointwise adaptive sampling baselines, with the largest gains in fields with highly localized complexity.
Comments22 pages. Accepted at NeurIPS 2026
