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Found 9 papers, 6 papers with code
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
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 #3 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)
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 #3 on
Image Classification
on PlantVillage
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage.
Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation
In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization.
SLSNet: Skin lesion segmentation using a lightweight generative adversarial network
Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model.
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images.
