Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
Open accessAbstract
Filter pruning has been widely applied to neural network compression and acceleration. Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune unimportant filters. There are two major limitations to these methods. First, existing methods fail to consider the variety of filter distribution across layers. To extract features of the coarse level to the fine level, the filters of different layers have various distributions. Therefore, it is not suitable to utilize the same pruning criteria to different functional layers. Second, prevailing layer-by-layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction. In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems. Specifically, we develop a differentiable pruning criteria sampler. This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria. In this way, we could adaptively select the appropriate pruning criteria for different functional layers. Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time. Experiments validate our approach on three image classification benchmarks. Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.
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Pruning is a well-studied topic in model compression, with many proposed methods.
“Pruning is the well-studied topic in model compression and many methods have been proposed to advance the network pruning [103].”
From Bringing ai to edge: from deep learning’s perspective (2022) · cites this paper as [103]
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Training Compact CNNs for Image Classification Using Dynamic-Coded Filter Fusion
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Iterative clustering pruning for convolutional neural networks
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Neural Network Structure Optimization by Simulated Annealing
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Rethinking the Pruning Criteria for Convolutional Neural Network
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GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization
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Prior Gradient Mask Guided Pruning-Aware Fine-Tuning
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DeepReDuce: ReLU Reduction for Fast Private InferencePreprint
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Interpretable Task-inspired Adaptive Filter Pruning for Neural Networks Under Multiple Constraints
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Filter Pruning via Learned Representation Median in the Frequency Domain
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Efficient Layer Compression Without Pruning
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Linearly Replaceable Filters for Deep Network Channel Pruning
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Multidimensional Pruning and Its Extension: A Unified Framework for Model Compression
Guo, J., Xu, D., & Ouyang, W. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3266435 · Cited by 30 · see citing works
Adaptive CNN filter pruning using global importance metric
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Implementation of Pavement Defect Detection System on Edge Computing Platform
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Automatic filter pruning algorithm for image classification
Xue, Y., Yao, W., Peng, S., & Yao, S. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05207-x · Cited by 27 · see citing works
Channel Pruning via Lookahead Search Guided Reinforcement Learning
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SOKS: Automatic Searching of the Optimal Kernel Shapes for Stripe-Wise Network Pruning
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Neural Network Pruning by Cooperative Coevolution
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Structural Alignment for Network Pruning through Partial Regularization
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Exploration and Estimation for Model Compression
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Exploiting Channel Similarity for Network Pruning
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On the Channel Pruning using Graph Convolution Network for Convolutional Neural Network Acceleration
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Filter Pruning via Feature Discrimination in Deep Neural Networks
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On the Effect of Pruning on Adversarial Robustness
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Loss-aware automatic selection of structured pruning criteria for deep neural network acceleration
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AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression
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Li, C., Li, H., Gao, G., Liu, Z., & Liu, P. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110326 · Cited by 22 · see citing works
Self-citationHolistic Filter Pruning for Efficient Deep Neural Networks
Enderich, L., Timm, F., & Burgard, W. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00264 · Cited by 21 · see citing works
Filter pruning via separation of sparsity search and model training
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Attentive Fine-Grained Structured Sparsity for Image Restoration
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WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks
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Soft Hybrid Knowledge Distillation against deep neural networks
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Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment
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Auto- Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
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REAF: Remembering Enhancement and Entropy-Based Asymptotic Forgetting for Filter Pruning
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Complex hybrid weighted pruning method for accelerating convolutional neural networks
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DMPP: Differentiable multi-pruner and predictor for neural network pruning
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Self-citationExploring Incompatible Knowledge Transfer in Few-shot Image Generation
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A multi-granularity CNN pruning framework via deformable soft mask with joint training
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Differentiable Transportation Pruning
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Kill Two Birds with One Stone: Domain Generalization for Semantic Segmentation via Network Pruning
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Self-citationProgressive kernel pruning with saliency mapping of input-output channels
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DDEP: Evolutionary pruning using distilled dataset
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AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
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Pruning-aware Sparse Regularization for Network Pruning
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Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference
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Model Lightweighting for Real-time Distraction Detection on Resource-Limited Devices
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FSConv: Flexible and separable convolution for convolutional neural networks compression
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Network pruning via probing the importance of filters
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Acceleration-Aware Fine-Grained Channel Pruning for Deep Neural Networks via Residual Gating
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Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps
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Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning
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RASP: Regularization-based Amplitude Saliency Pruning
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Cross-layer importance evaluation for neural network pruning
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Batch Normalization Tells You Which Filter is Important
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Image-Captioning Model Compression
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CorrNet: pearson correlation based pruning for efficient convolutional neural networks
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- Authors
- He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., & Yang, Y.
- Affiliations
- University of Technology Sydney (AU)
- Baidu (China) (CN)
- Nanyang Technological University (SG)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 2006-2015
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035467254
- Cited by
- 262
- References
- 64
- Citations by year
- 2026: 9 · 2025: 35 · 2024: 43 · 2023: 62 · 2022: 55 · 2021: 49 · 2020: 9
- FWCI
- 13.50
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- filter pruning, neural network compression, deep convolutional neural networks, layer-wise pruning, model acceleration, FLOPs reduction, image classification, ResNet50
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