Network Pruning via Transformable Architecture Search
Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the pruned networks, and then transfer parameters from the unpruned network to pruned networks. To break the structure limitation of the pruned networks, we propose to apply neural architecture search to search directly for a net
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Channel Pruning via Optimal Thresholding
Pruning from Scratch
GitHub - he-y/Awesome-Pruning: A curated list of neural network pruning resources.
Importance Estimation for Neural Network Pruning
A Closer Look at Structured Pruning for Neural Network Compression
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
Building Efficient ConvNets using Redundant Feature Pruning
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