Competitive Inner-Imaging Squeeze and Excitation for Residual Network Article Swipe
Yang Hu
,
Guihua Wen
,
Mingnan Luo
,
Dan Dai
,
Jiajiong Ma
,
Zhiwen Yu
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1807.08920
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1807.08920
Residual networks, which use a residual unit to supplement the identity mappings, enable very deep convolutional architecture to operate well, however, the residual architecture has been proved to be diverse and redundant, which may leads to low-efficient modeling. In this work, we propose a competitive squeeze-excitation (SE) mechanism for the residual network. Re-scaling the value for each channel in this structure will be determined by the residual and identity mappings jointly, and this design enables us to expand the meaning of channel relationship modeling in residual blocks. Modeling of the competition between residual and identity mappings cause the identity flow to control the complement of the residual feature maps for itself. Furthermore, we design a novel inner-imaging competitive SE block to shrink the consumption and re-image the global features of intermediate network structure, by using the inner-imaging mechanism, we can model the channel-wise relations with convolution in spatial. We carry out experiments on the CIFAR, SVHN, and ImageNet datasets, and the proposed method can challenge state-of-the-art results.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1807.08920
- https://arxiv.org/pdf/1807.08920
- OA Status
- green
- Cited By
- 46
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2884188791
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2884188791Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1807.08920Digital Object Identifier
- Title
-
Competitive Inner-Imaging Squeeze and Excitation for Residual NetworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-07-24Full publication date if available
- Authors
-
Yang Hu, Guihua Wen, Mingnan Luo, Dan Dai, Jiajiong Ma, Zhiwen YuList of authors in order
- Landing page
-
https://arxiv.org/abs/1807.08920Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1807.08920Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1807.08920Direct OA link when available
- Concepts
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Residual, Computer science, Convolution (computer science), Block (permutation group theory), Identity (music), Convolutional neural network, Artificial intelligence, Algorithm, Pattern recognition (psychology), Artificial neural network, Mathematics, Geometry, Acoustics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
46Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 8, 2022: 2, 2021: 12Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.CIFAR, | 154 |
| abstract_inverted_index.design | 73, 113 |
| abstract_inverted_index.enable | 12 |
| abstract_inverted_index.expand | 77 |
| abstract_inverted_index.global | 127 |
| abstract_inverted_index.method | 162 |
| abstract_inverted_index.proved | 26 |
| abstract_inverted_index.shrink | 121 |
| abstract_inverted_index.between | 91 |
| abstract_inverted_index.blocks. | 86 |
| abstract_inverted_index.channel | 57, 81 |
| abstract_inverted_index.control | 101 |
| abstract_inverted_index.diverse | 29 |
| abstract_inverted_index.enables | 74 |
| abstract_inverted_index.feature | 107 |
| abstract_inverted_index.itself. | 110 |
| abstract_inverted_index.meaning | 79 |
| abstract_inverted_index.network | 131 |
| abstract_inverted_index.operate | 18 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.ImageNet | 157 |
| abstract_inverted_index.Modeling | 87 |
| abstract_inverted_index.Residual | 0 |
| abstract_inverted_index.features | 128 |
| abstract_inverted_index.however, | 20 |
| abstract_inverted_index.identity | 10, 68, 94, 98 |
| abstract_inverted_index.jointly, | 70 |
| abstract_inverted_index.mappings | 69, 95 |
| abstract_inverted_index.modeling | 83 |
| abstract_inverted_index.network. | 51 |
| abstract_inverted_index.proposed | 161 |
| abstract_inverted_index.re-image | 125 |
| abstract_inverted_index.residual | 5, 22, 50, 66, 85, 92, 106 |
| abstract_inverted_index.results. | 166 |
| abstract_inverted_index.spatial. | 147 |
| abstract_inverted_index.challenge | 164 |
| abstract_inverted_index.datasets, | 158 |
| abstract_inverted_index.mappings, | 11 |
| abstract_inverted_index.mechanism | 47 |
| abstract_inverted_index.modeling. | 37 |
| abstract_inverted_index.networks, | 1 |
| abstract_inverted_index.relations | 143 |
| abstract_inverted_index.structure | 60 |
| abstract_inverted_index.Re-scaling | 52 |
| abstract_inverted_index.complement | 103 |
| abstract_inverted_index.determined | 63 |
| abstract_inverted_index.mechanism, | 137 |
| abstract_inverted_index.redundant, | 31 |
| abstract_inverted_index.structure, | 132 |
| abstract_inverted_index.supplement | 8 |
| abstract_inverted_index.competition | 90 |
| abstract_inverted_index.competitive | 44, 117 |
| abstract_inverted_index.consumption | 123 |
| abstract_inverted_index.convolution | 145 |
| abstract_inverted_index.experiments | 151 |
| abstract_inverted_index.Furthermore, | 111 |
| abstract_inverted_index.architecture | 16, 23 |
| abstract_inverted_index.channel-wise | 142 |
| abstract_inverted_index.intermediate | 130 |
| abstract_inverted_index.relationship | 82 |
| abstract_inverted_index.convolutional | 15 |
| abstract_inverted_index.inner-imaging | 116, 136 |
| abstract_inverted_index.low-efficient | 36 |
| abstract_inverted_index.state-of-the-art | 165 |
| abstract_inverted_index.squeeze-excitation | 45 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |