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Symmetric Convolutional Filters: A Novel Way to Constrain Parameters in CNN

arXiv 2202.13099 ↗First posted 26 Feb 2022cs.CVcited by 0

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Abstract (arXiv)

We propose a novel technique to constrain parameters in CNN based on symmetric filters. We investigate the impact on SOTA networks when varying the combinations of symmetricity. We demonstrate that our models offer effective generalisation and a structured elimination of redundancy in parameters. We conclude by comparing our method with other pruning techniques.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 26 Feb 2022.

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References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  4. 2014Going Deeper with Convolutionscited by 4,241
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  7. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  8. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  9. 2016Pruning Filters for Efficient ConvNetscited by 1,143
  10. 2016An overview of gradient descent optimization algorithmscited by 1,033
  11. 2020HRank: Filter Pruning using High-Rank Feature Mapcited by 292
  12. 2017NISP: Pruning Networks using Neuron Importance Score Propagationcited by 228
1 more
  1. 2013Predicting Parameters in Deep Learningcited by 224

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