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Existing methods of pruning deep neural networks focus on removingunnecessary parameters of the trained network and fine tuning the modelafterwards to find a good solution that recovers the initial performance of thetrained model. Unlike other works, our method pays special attention to thequality of the solution in the compressed model and inference computation timeby pruning neurons. The proposed algorithm directs the parameters of thecompressed model toward a flatter solution by exploring the spectral radius ofHessian which results in better generalization on unseen data. Moreover, themethod does not work with a pre-trained network and performs training andpruning simultaneously. Our result shows that it improves the state-of-the-artresults on neuron compression. The method is able to achieve very smallnetworks with small accuracy degradation across different neural networkmodels.

Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation