The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Diverse Branch Block: Building a Convolution as an Inception-like Unit
Open accessAbstract
We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multiscale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https://github.com/DingXiaoH/DiverseBranchBlock.
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Cites
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Self-citationImageNet classification with deep convolutional neural networks
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Self-citationThe Cityscapes Dataset for Semantic Urban Scene Understanding
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Squeeze-and-Excitation Networks
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ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
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“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.”
BackgroundMethodYOLOv7: 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.”
BackgroundMethodMobileOne: An Improved One millisecond Mobile Backbone
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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results
Li, Y., Zhang, Y., Timofte, R., Van Gool, L., Yu, L., Li, Y., Li, X., Jiang, T., Wu, Q., Han, M., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00189 · Cited by 72
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Metadata
- Authors
- Ding, X., Zhang, X., Han, J., & Ding, G.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 10881-10890
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3171038842
- Cited by
- 520
- References
- 14
- FWCI
- 24.60
- Type
- conference paper
- Language
- en
- Open access
- green
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