Structured Pruning of Neural Networks With Budget-Aware Regularization
Open accessAbstract
Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact. Among the most effective methods are those that prune a network while training it with a sparsity prior loss and learnable dropout parameters. A shortcoming of these approaches however is that neither the size nor the inference speed of the pruned network can be controlled directly; yet this is a key feature for targeting deployment of CNNs on low-power hardware. To overcome this, we introduce a budgeted regularized pruning framework for deep CNNs. Our approach naturally fits into traditional neural network training as it consists of a learnable masking layer, a novel budget-aware objective function, and the use of knowledge distillation. We also provide insights on how to prune a residual network and how this can lead to new architectures. Experimental results reveal that CNNs pruned with our method are more accurate and less compute-hungry than state-of-the-art methods. Also, our approach is more effective at preventing accuracy collapse in case of severe pruning; this allows pruning factors of up to 16× without significant accuracy drop.
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Many methods based on the sparse learning (SL) framework have been proven to be effective.
“Many methods based on the sparse learning (SL) framework have also been proven to be effective [43-47]”
From A survey of model compression strategies for object detection (2023) · cites this paper as [47]
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- Authors
- Lemaire, C., Achkar, A., & Jodoin, P.-M.
- Affiliations
- Université de Sherbrooke (CA)
- Miovision Technologies Inc., Canada
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 9100-9108
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2900763636
- Cited by
- 90
- References
- 40
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 17 · 2023: 14 · 2022: 15 · 2021: 21 · 2020: 14 · 2019: 1
- FWCI
- 4.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Compute Canada
- Mitacs
- 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
- neural network pruning, knowledge distillation, convolutional neural networks, ResNet, low-power deployment, inference speed, model compression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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