You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Feb 25, 2020. It is now read-only.
I read the codes in it and find that they just set the connection weights as zeros. And then use the whole matrix to calculate the output. I assume that when training and testing the network, the memory footprint(gpu memory) would not change.
Therefore, how pruning benefit the CNN when applying in resource limited machine?
The text was updated successfully, but these errors were encountered:
Sign up for freeto subscribe to this conversation on GitHub.
Already have an account?
Sign in.
I read the codes in it and find that they just set the connection weights as zeros. And then use the whole matrix to calculate the output. I assume that when training and testing the network, the memory footprint(gpu memory) would not change.
Therefore, how pruning benefit the CNN when applying in resource limited machine?
The text was updated successfully, but these errors were encountered: