How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability
A new research paper investigates how the allocation of sparsity in neural networks impacts their ability to recover accuracy after pruning, especially when labeled retraining data is unavailable. The study compares different sparsity allocation methods like ERK and LAMP across various datasets and architectures, finding that the choice of allocation significantly affects post-repair accuracy. Researchers identified a critical transition regime where standard repair methods begin to fail, highlighting the need to jointly consider pruning allocation and repair strategies. AI
IMPACT Investigates methods to maintain neural network performance after aggressive pruning, crucial for efficient deployment in resource-constrained environments.