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Reduced storage direct tensor ring decomposition for convolutional neural networks compression

arXiv 2405.10802 ↗First posted 17 May 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Convolutional neural networks (CNNs) are among the most widely used machine learning models for computer vision tasks, such as image classification. To improve the efficiency of CNNs, many CNNs compressing approaches have been developed. Low-rank methods approximate the original convolutional kernel with a sequence of smaller convolutional kernels, which leads to reduced storage and time complexities. In this study, we propose a novel low-rank CNNs compression method that is based on reduced storage direct tensor ring decomposition (RSDTR). The proposed method offers a higher circular mode permutation flexibility, and it is characterized by large parameter and FLOPS compression rates, while preserving a good classification accuracy of the compressed network. The experiments, performed on the CIFAR-10 and ImageNet datasets, clearly demonstrate the efficiency of RSDTR in comparison to other state-of-the-art CNNs compression approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 65 of its 122 references that are in the map. First posted on arXiv 17 May 2024.

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65 of 122 references.

References · 65

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  5. 2022A ConvNet for the 2020scited by 3,579
  6. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  7. 2016Pruning Filters for Efficient ConvNetscited by 1,143
  8. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  9. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 712
  10. 2017Learning Efficient Convolutional Networks through Network Slimmingcited by 676
  11. 2019Importance Estimation for Neural Network Pruningcited by 541
  12. 2018Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Accelerationcited by 367
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  1. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  2. 2018Soft Filter Pruning for Accelerating Deep Convolutional Neural Networkscited by 315
  3. 2020HRank: Filter Pruning using High-Rank Feature Mapcited by 292
  4. 2018Deep Face Recognition: A Surveycited by 276
  5. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  6. 2017NISP: Pruning Networks using Neuron Importance Score Propagationcited by 228
  7. 2014Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decompositioncited by 203
  8. 2023Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIPcited by 201
  9. 2018Discrimination-aware Channel Pruning for Deep Neural Networkscited by 171
  10. 2015Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applicationscited by 162
  11. 2018Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Losscited by 160
  12. 2015Compressing Neural Networks with the Hashing Trickcited by 150
  13. 2019Towards Optimal Structured CNN Pruning via Generative Adversarial Learningcited by 146
  14. 2021ElasticFace: Elastic Margin Loss for Deep Face Recognitioncited by 144
  15. 2016Tensor Ring Decompositioncited by 127
  16. 2019EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasiscited by 98
  17. 2019Provable Filter Pruning for Efficient Neural Networkscited by 98
  18. 2020SCOP: Scientific Control for Reliable Neural Network Pruningcited by 93
  19. 2020Channel Pruning via Automatic Structure Searchcited by 85
  20. 2017Towards Effective Low-bitwidth Convolutional Neural Networkscited by 78
  21. 2021CHIP: CHannel Independence-based Pruning for Compact Neural Networkscited by 78
  22. 2020Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Networkcited by 74
  23. 2019Pruning from Scratchcited by 72
  24. 2017More is Less: A More Complicated Network with Less Inference Complexitycited by 66
  25. 2016Ultimate tensorization: compressing convolutional and FC layers alikecited by 56
  26. 2020Discrimination-aware Network Pruning for Deep Model Compressioncited by 51
  27. 2018Wide Compression: Tensor Ring Netscited by 50
  28. 2019Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimationcited by 40
  29. 2021Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decompositioncited by 33
  30. 2020TRP: Trained Rank Pruning for Efficient Deep Neural Networkscited by 32
  31. 2017CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compressioncited by 30
  32. 2021DECORE: Deep Compression with Reinforcement Learningcited by 29
  33. 2020Heuristic Rank Selection with Progressively Searching Tensor Ring Networkcited by 28
  34. 2018Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networkscited by 27
  35. 2021Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Predictioncited by 24
  36. 2018Trained Rank Pruning for Efficient Deep Neural Networkscited by 23
  37. 2020Filter Sketch for Network Pruningcited by 20
  38. 2019Filter Pruning by Switching to Neighboring CNNs with Good Attributescited by 15

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