Taylor-expansion importance scoring enables layer-agnostic pruning of neural networks that outperforms prior methods on ImageNet accuracy-FLOPs trade-offs.
Iterative pruning clearly outperforms other settings over all epochs
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Importance Estimation for Neural Network Pruning
Taylor-expansion importance scoring enables layer-agnostic pruning of neural networks that outperforms prior methods on ImageNet accuracy-FLOPs trade-offs.