The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
Open accessAbstract
As designing appropriate Convolutional Neural Network (CNN) architecture in the context of a given application usually involves heavy human works or numerous GPU hours, the research community is soliciting the architecture-neutral CNN structures, which can be easily plugged into multiple mature architectures to improve the performance on our real-world applications. We propose Asymmetric Convolution Block (ACB), an architecture-neutral structure as a CNN building block, which uses 1D asymmetric convolutions to strengthen the square convolution kernels. For an off-the-shelf architecture, we replace the standard square-kernel convolutional layers with ACBs to construct an Asymmetric Convolutional Network (ACNet), which can be trained to reach a higher level of accuracy. After training, we equivalently convert the ACNet into the same original architecture, thus requiring no extra computations anymore. We have observed that ACNet can improve the performance of various models on CIFAR and ImageNet by a clear margin. Through further experiments, we attribute the effectiveness of ACB to its capability of enhancing the model's robustness to rotational distortions and strengthening the central skeleton parts of square convolution kernels.
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Cites
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Squeeze-and-Excitation Networks
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Rethinking the Inception Architecture for Computer Vision
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Squeeze-and-Excitation Networks
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Cited by
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Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00721 · Cited by 13,198
“Model re-parametrization techniques [71, 31, 75, 19, 33, 11, 4, 24, 13, 12, 10, 29, 14, 78] merge multiple computational modules into one at inference stage.”
MethodYOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022) · arXiv · doi:10.48550/arxiv.2207.02696 · Cited by 13,205
“Model re-parametrization techniques [71, 31, 75, 19, 33, 11, 4, 24, 13, 12, 10, 29, 14, 78] merge multiple computational modules into one at inference stage.”
MethodRepVGG: Making VGG-style ConvNets Great Again
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Self-citationCOVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020) · IEEE Access · doi:10.1109/access.2020.3016780 · Cited by 472
Metadata
- Authors
- Ding, X., Guo, Y., Ding, G., & Han, J.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1911-1920
- OpenAlex
- W2981609437
- Cited by
- 912
- References
- 14
- FWCI
- 23.61
- Type
- conference paper
- Language
- en
- Open access
- green
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