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Determining an appropriate number of features for each layer in a neuralnetwork is an important and difficult task. This task is especially importantin applications on systems with limited memory or processing power. Manycurrent approaches to reduce network size either utilize iterative procedures,which can extend training time significantly, or require very careful tuning ofalgorithm parameters to achieve reasonable results. In this paper we proposeNodeDrop, a new method for eliminating features in a network. With NodeDrop, wedefine a condition to identify and guarantee which nodes carry no information,and then use regularization to encourage nodes to meet this condition. We findthat NodeDrop drastically reduces the number of features in a network whilemaintaining high performance, reducing the number of parameters by a factor of114x for a VGG like network on CIFAR10 without a drop in accuracy.

NodeDrop: A Condition for Reducing Network Size without Effect on Output