Methods › Computer Vision › Convolutions › Active Convolution
Active Convolution
Introduced by Yunho Jeon et al. in Active Convolution: Learning the Shape of Convolution for Image Classification
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
An Active Convolution is a type of convolution which does not have a fixed shape of the receptive field, and can be used to take more diverse forms of receptive fields for convolutions. Its shape can be learned through backpropagation during training. It can be seen as a generalization of convolution; it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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CodeTF: One-stop Transformer Library for State-of-the-art Code LLM 31 May 2023 · 1 repository · arXiv:2306.00029
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Integrating Multiple Receptive Fields through Grouped Active Convolution 11 Nov 2018 · 0 repositories · arXiv:1811.04387
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Active Convolution: Learning the Shape of Convolution for Image Classification 27 Mar 2017 · 1 repository · arXiv:1703.09076
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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