ArticleComputational and mathematical methods in medicine2020
Research and Verification of Convolutional Neural Network Lightweight in BCI.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot's Picking Clustered Fruits of Chilies.Sensors (Basel, Switzerland) · 2023Article
- SEALNET: Facial recognition software for ecological studies of harbor seals.Ecology and evolution · 2022Article
- Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis.Computational intelligence and neuroscience · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.