Sampling by Target-Neighborhood Sensitivity for Accelerated Implicit Neural Representations

Implicit Neural Representations (INRs) learn underlying continuous target functions from discrete observations, but full-coordinate training is typically time-intensive. To this end, we introduce Sampling by Target-Neighborhood Sensitivity (STaNS), a lightweight strategy that dynamically samples an informative coordinate subset for accelerated INR training. Our key insight is simple: we view each observed coordinate as a target-neighborhood (TaN) representative of the underlying continuous target function. To characterize the local informativeness of a representative, we define TaN mismatch as the discrepancy between the current INR prediction anchored at that coordinate and target values at locally offset coordinates. We then define TaN sensitivity as the worst first-order growth of this mismatch as the offset becomes infinitesimal, capturing how rapidly the anchored prediction loses consistency with the local target behavior. During INR training, STaNS dynamically updates coordinate-wise TaN scores derived from the closed-form TaN sensitivity for probabilistic sampling, prioritizing informative coordinates to accelerate training. Validated across audio, image, and shape modalities, STaNS establishes state-of-the-art INR acceleration, reducing time-to-quality by up to over existing samplers and delivering up to speedup over full-coordinate training.