Learning Both Weights And Connections For Efficient Neural Networks. 1)pruning is a method to improve the energy efficiency and storage of neural networks without affecting accuracy by finding the right connections. Next, we prune the unimportant connections.
【Learning both Weights and Connections for Efficient Neural Networks】论文 from blog.csdn.net
Finally, we retrain the network to fine tune the weights of the. A method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important. Next, we prune the unimportant connections.
Learning Both Weights And Connections For Efficient Neural Networks.
First, we train the network to learn which connections are important. Finally, we retrain the network to fine tune the weights of the. First, we train the network to learn which connections are important.
Next, We Prune The Unimportant.
1)pruning is a method to improve the energy efficiency and storage of neural networks without affecting accuracy by finding the right connections. All connections with weights below a threshold are removed from the network — converting a dense network into a sparse. A method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important.
