"our
method prunes redundant connections using a three-step method. First, we train
the network to learn which connections are important. Next, we prune the unim-
portant connections. Finally, we retrain the network to fine tune the weights of the
remaining connections. On the ImageNet dataset, our method reduced the number
of parameters of AlexNet by a factor of
9
×
, from 61 million to 6.7 million, without
incurring accuracy loss. Similar experiments with VGG-16 found that the total
number of parameters can be reduced by
13
×
, from 138 million to 10.3 million,
again with no loss of accuracy.
"
"our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unim- portant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9 × , from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the total number of parameters can be reduced by 13 × , from 138 million to 10.3 million, again with no loss of accuracy. "