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Found 10 papers, 7 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 #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)
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
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.
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram.
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition. Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE.
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography.
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
