The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
More is Less: A More Complicated Network with Less Inference Complexity
Open accessAbstract
In this paper, we present a novel and general network structure towards accelerating the inference process of convolutional neural networks, which is more complicated in network structure yet with less inference complexity. The core idea is to equip each original convolutional layer with another low-cost collaborative layer (LCCL), and the element-wise multiplication of the ReLU outputs of these two parallel layers produces the layer-wise output. The combined layer is potentially more discriminative than the original convolutional layer, and its inference is faster for two reasons: 1) the zero cells of the LCCL feature maps will remain zero after element-wise multiplication, and thus it is safe to skip the calculation of the corresponding high-cost convolution in the original convolutional layer; 2) LCCL is very fast if it is implemented as a 1 × 1 convolution or only a single filter shared by all channels. Extensive experiments on the CIFAR-10, CIFAR-100 and ILSCRC-2012 benchmarks show that our proposed network structure can accelerate the inference process by 32% on average with negligible performance drop.
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ImageNet classification with deep convolutional neural networks
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Identity Mappings in Deep Residual Networks
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Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
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Self-citationDynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
Searching for a Robust Neural Architecture in Four GPU Hours
Dong, X., & Yang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00186 · Cited by 640
Self-citationWhat is the State of Neural Network Pruning?
Blalock, D., Ortiz, J. J. G., Frankle, J., & Guttag, J. (2020) · arXiv · doi:10.48550/arxiv.2003.03033 · Cited by 560
Metadata
- Authors
- Dong, X., Huang, J., Yang, Y., & Yan, S.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1895-1903
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2604998962
- Cited by
- 291
- References
- 12
- FWCI
- 8.90
- Type
- conference paper
- Language
- en
- Open access
- green
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- Dong, X., Huang, J., Yang, Y., & Yan, S. (2017). More is Less: A More Complicated Network with Less Inference Complexity. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.205
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