CGaP: Continuous Growth and Pruning for Efficient Deep Learning

27 May 2019Xiaocong DuZheng LiYu Cao

Today a canonical approach to reduce the computation cost of Deep Neural Networks (DNNs) is to pre-define an over-parameterized model before training to guarantee the learning capacity, and then prune unimportant learning units (filters and neurons) during training to improve model compactness. We argue it is unnecessary to introduce redundancy at the beginning of the training but then reduce redundancy for the ultimate inference model... (read more)

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