What is recorded here?
Introduced DenseNet, connecting each convolutional layer to preceding feature maps to encourage feature reuse and improve gradient propagation.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The first preprint appeared in 2016; year follows CVPR 2017 publication. Reported efficiency and accuracy are dataset-specific.
Read the original work
- Huang and colleagues: densely connected convolutional networks — original paper
- Huang and colleagues: densely connected convolutional networks — official research page
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Gao Huang; Zhuang Liu; Laurens van der Maaten; Kilian Q. Weinberger
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.
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English catalogue narrative
Huang and colleagues: densely connected convolutional networks
Introduced DenseNet, connecting each convolutional layer to preceding feature maps to encourage feature reuse and improve gradient propagation.
Scientific scope
The first preprint appeared in 2016; year follows CVPR 2017 publication. Reported efficiency and accuracy are dataset-specific.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Gao Huang; Zhuang Liu; Laurens van der Maaten; Kilian Q. Weinberger
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.