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Found 5 papers, 5 papers with code
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks
In Deep Convolutional Neural Networks (DCNNs), the parameter count in pointwise convolutions quickly grows due to the multiplication of the filters and input channels from the preceding layer.
Ranked #1 on
Image Classification
on PlantDoc
Color-aware two-branch DCNN for efficient plant disease classification
Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection.
Ranked #4 on
Image Classification
on PlantVillage
An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints
The number of groups of filters and filters per group for layers K and L is determined by exact divisions of the original number of input channels and filters by Ch.
Ranked #1 on
Image Classification
on Malaria Dataset
Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches
The Food and Agriculture Organization (FAO) estimated that plant diseases cost the world economy $220 billion in 2019.
Ranked #4 on
Image Classification
on PlantVillage
Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet
Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer.
Ranked #5 on
Image Classification
on Oxford-IIIT Pet Dataset
(PARAMS metric)
