Growing Efficient Deep Networks by Structured Continuous Sparsification

30 Jul 2020Xin YuanPedro SavareseMichael Maire

We develop an approach to training deep networks while dynamically adjusting their architecture, driven by a principled combination of accuracy and sparsity objectives. Unlike conventional pruning approaches, our method adopts a gradual continuous relaxation of discrete network structure optimization and then samples sparse subnetworks, enabling efficient deep networks to be trained in a growing and pruning manner... (read more)

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