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