COSP: Consumer Oriented Second-order Pruning for Compact Implicit Neural Representations

Implicit neural representations (INRs) encode a signal as a coordinate network. We study post-training structured compaction, which requires identifying hidden structures that can be removed with little functional damage. This is difficult because deleting a neuron changes adjacent affine tensors, whereas conventional group scores need not match the underlying functional perturbation and globally comparable second-order scores are costly. We propose COSP (Consumer Oriented Second-order Pruning) for structured INR compaction. COSP derives a module-wise second-order back-propagation rule and its diagonal propagation variant, which evaluate candidate groups without materializing a global parameter Hessian. It then defines a consumer-side group that captures the functional perturbation of removing its associated full dependency group, selects groups globally under a physical parameter budget, and applies ridge reconstruction after selection. We validate COSP through comprehensive experiments: (1) verifying its second-order derivative propagation and consumer-side saliency, (2) comparing with established pruning baselines, and (3) ablation studies on key design choices and computational cost. COSP achieves the best mean decoded quality for every evaluated modality and parameter reduction, and ranks first in 125 of 132 matched evaluation settings.