Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network Article Swipe
Enjie Ding
,
Yuhao Cheng
,
Chengcheng Xiao
,
Zhongyu Liu
,
Wanli Yu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11073111
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11073111
Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters.
Related Topics
Concepts
Computer science
Information loss
Convolutional neural network
Redundancy (engineering)
Pooling
Convolution (computer science)
Channel (broadcasting)
Reduction (mathematics)
Feature (linguistics)
Kernel (algebra)
Artificial intelligence
Pattern recognition (psychology)
Data mining
Algorithm
Artificial neural network
Computer network
Mathematics
Combinatorics
Geometry
Operating system
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app11073111
- https://www.mdpi.com/2076-3417/11/7/3111/pdf?version=1617938788
- OA Status
- gold
- Cited By
- 6
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3144104606
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3144104606Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app11073111Digital Object Identifier
- Title
-
Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-31Full publication date if available
- Authors
-
Enjie Ding, Yuhao Cheng, Chengcheng Xiao, Zhongyu Liu, Wanli YuList of authors in order
- Landing page
-
https://doi.org/10.3390/app11073111Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/11/7/3111/pdf?version=1617938788Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/11/7/3111/pdf?version=1617938788Direct OA link when available
- Concepts
-
Computer science, Information loss, Convolutional neural network, Redundancy (engineering), Pooling, Convolution (computer science), Channel (broadcasting), Reduction (mathematics), Feature (linguistics), Kernel (algebra), Artificial intelligence, Pattern recognition (psychology), Data mining, Algorithm, Artificial neural network, Computer network, Mathematics, Combinatorics, Geometry, Operating system, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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