  Residual Networks with convolutional layers are widely used in the field of
machine learning. Since they effectively extract features from input data by
stacking multiple layers, they can achieve high accuracy in many applications.
However, the stacking of many layers raises their computation costs. To address
this problem, we propose Network Implosion, it erases multiple layers from
Residual Networks without degrading accuracy. Our key idea is to introduce a
priority term that identifies the importance of a layer; we can select
unimportant layers according to the priority and erase them after the training.
In addition, we retrain the networks to avoid critical drops in accuracy after
layer erasure. A theoretical assessment reveals that our erasure and retraining
scheme can erase layers without accuracy drop, and achieve higher accuracy than
is possible with training from scratch. Our experiments show that Network
Implosion can, for classification on Cifar-10/100 and ImageNet, reduce the
number of layers by 24.00 to 42.86 percent without any drop in accuracy.
