Nov 19, 2019 · We propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and decoding efficiency.
The novel block-max weight masking (BMWM) method is proposed, which can effectively retain the salient weights while imposing high regularity to the weight ...
As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and ...
Neural network pruning, as one of the mainstream model compression techniques, is under extensive study to reduce the number of parameters and computations. In ...
Our experimental results show that DARB can achieve 13× to 25× pruning ratio, which are. 2.8× to 4.3× improvements than the state-of-the-art counter- parts on ...
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To prune, or not to prune: exploring the efficacy of pruning for model compression [✏️✏️paper] ... DARB: A Density-Aware Regular-Block Pruning for Deep Neural ...
As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and ...
Reborn filters: Pruning convolutional neural networks with limited data, AAAI, F, -. DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks, AAAI ...
Aug 8, 2024 · Bibliographic details on DARB: A Density-Adaptive Regular-Block Pruning for Deep Neural Networks.
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As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and ...