mlsanityData to 14 Oct 2026

D-Score: A Synapse-Inspired Approach for Filter Pruning

arXiv 2308.04470 ↗First posted 8 Aug 2023cs.NE cs.LGcited by 0

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Abstract (arXiv)

This paper introduces a new aspect for determining the rank of the unimportant filters for filter pruning on convolutional neural networks (CNNs). In the human synaptic system, there are two important channels known as excitatory and inhibitory neurotransmitters that transmit a signal from a neuron to a cell. Adopting the neuroscientific perspective, we propose a synapse-inspired filter pruning method, namely Dynamic Score (D-Score). D-Score analyzes the independent importance of positive and negative weights in the filters and ranks the independent importance by assigning scores. Filters having low overall scores, and thus low impact on the accuracy of neural networks are pruned. The experimental results on CIFAR-10 and ImageNet datasets demonstrate the effectiveness of our proposed method by reducing notable amounts of FLOPs and Params without significant Acc. Drop.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 42 references that are in the map. First posted on arXiv 8 Aug 2023.

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28 of 42 references.

References · 28

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16 more
  1. 2017ThiNet: A Filter Level Pruning Method for Deep Neural Network Compressioncited by 442
  2. 2018Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Accelerationcited by 367
  3. 2018Soft Filter Pruning for Accelerating Deep Convolutional Neural Networkscited by 315
  4. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  5. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 301
  6. 2020HRank: Filter Pruning using High-Rank Feature Mapcited by 292
  7. 2016Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruningcited by 134
  8. 2018Dynamic Channel Pruning: Feature Boosting and Suppressioncited by 129
  9. 2019Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networkscited by 118
  10. 2015Data-free parameter pruning for Deep Neural Networkscited by 112
  11. 2018Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layerscited by 112
  12. 2019Provable Filter Pruning for Efficient Neural Networkscited by 98
  13. 2017More is Less: A More Complicated Network with Less Inference Complexitycited by 66
  14. 2019Play and Prune: Adaptive Filter Pruning for Deep Model Compressioncited by 15
  15. 2018SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Mannerscited by 7
  16. 2016Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networkscited by 6

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