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Keyword: alternating direction method of multipliers ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

   Lin, Ji, Li, Deng and Li · arXiv (Cornell University) · 2019

   The paper proposes structured sparsity regularisation to prune filters from CNNs, solved with an ADMM-style method called AULM, and reports better results than state-of-the-art methods on several networks and tasks.

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   1. UncheckedThe AULM solver, built on ADMM, is reported to be about 2.5 times more efficient than recent work that directly solves group-sparsity regularisation for CNN pruning.“AULM follows the principle of ADMM and alternates between promoting the structured sparsity of CNNs and optimizing the recognition loss, which leads to a very efficient solver (2.5x to the most recent work that directly solves the group sparsity-based regularization).”
   2. Unchecked“We further demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which also show exciting performance gains over the state-of-the-arts.”