Pruning neural networks: is it time to nip it in the bud?
Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed.However, the value of pruning has largely evaded scrutiny.In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations.First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network.Second, it is the architectures obtained through the pruning process --- not the learnt weights --- that prove valuable.Such architectures are powerful when trained from scratch.Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.
Training small networks beats pruning, but pruning finds good small networks to train that are easy to copy.