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

Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining