How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability

Post-training pruning involves two coupled design choices: how sparsity is allocated across layers and how the resulting sparse model is repaired. We study whether these choices can be treated independently when recovery must be label-free, using only a small unlabeled calibration set. Across ResNet-18/34/50 on CIFAR-10, CIFAR-100, and Imagenette, we combine four unstructured sparsity-allocation strategies with the same suite of gradient-free repair methods. We find that allocation changes both the attainable repaired accuracy and the repair method that works best, with the strongest interaction appearing in a repair-sensitive transition regime between stable recovery and collapse. Across 27 main ResNet settings, the best repair method changes across allocations in 21 settings, and allocation changes the recoverability regime in 13. We further introduce LDMS-σ, a calibration-only diagnostic based on dense–pruned activation statistics, which selects the best repaired allocation in 25 of 27 settings and remains within one percentage point in 26. These results show that post-pruning recoverability is a property of the allocation–repair pair rather than either component alone.