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Global Neuron Importance Estimation is used to prune neural networks forefficiency reasons. To determine the global importance of each neuron orconvolutional kernel, most of the existing methods either use activation orgradient information or both, which demands abundant labelled examples. In thiswork, we use heuristics to derive importance estimation similar to Taylor FirstOrder (TaylorFO) approximation based methods. We name our methods TaylorFO-absand TaylorFO-sq. We propose two additional methods to improve these importanceestimation methods. Firstly, we propagate random gradients from the last layerof a network, thus avoiding the need for labelled examples. Secondly, wenormalize the gradient magnitude of the last layer output before propagating,which allows all examples to contribute similarly to the importance score. Ourmethods with additional techniques perform better than previous methods whentested on ResNet and VGG architectures on CIFAR-100 and STL-10 datasets.Furthermore, our method also complements the existing methods and improvestheir performances when combined with them.

Importance Estimation with Random Gradient for Neural Network Pruning