The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Fast ConvNets Using Group-Wise Brain Damage
Abstract
We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers in ConvNets. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion. After such pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. We investigate different ways to add group-wise prunning to the learning process, and show that severalfold speedups of convolutional layers can be attained using group-sparsity regularizers. Our approach can adjust the shapes of the receptive fields in the convolutional layers, and even prune excessive feature maps from ConvNets, all in data-driven way.
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Cites
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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“2x speedup, we have found a negligible drop in performance for the INRIA holidays dataset [23] from 0.”
BackgroundConvolutional neural networks at constrained time cost
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Cited by
Pruning Filters for Efficient ConvNets
Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2016) · arXiv · doi:10.48550/arxiv.1608.08710 · Cited by 2,928
Channel Pruning for Accelerating Very Deep Neural Networks
He, Y., Zhang, X., & Sun, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.155 · Cited by 2,647
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
He, Y., Kang, G., Dong, X., Fu, Y., & Yang, Y. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/309 · Cited by 1,020
Importance Estimation for Neural Network Pruning
Molchanov, P., Mallya, A., Tyree, S., Frosio, I., & Kautz, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01152 · Cited by 983
Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765695 · Cited by 817
Metadata
- Authors
- Lebedev, V., & Lempitsky, V.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2554-2564
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W566555209
- Cited by
- 373
- References
- 12
- FWCI
- 16.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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