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Found 10 papers, 3 papers with code
Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels
Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures, that surpass the performance of classical CNNs, by a considerable scalability and accuracy margin.
Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields
In this study, we present evidence suggesting that depthwise convolutional kernels are effectively replicating the structural intricacies of the biological receptive fields observed in the mammalian retina.
Prediction of Tourism Flow with Sparse Geolocation Data
Modern tourism in the 21st century is facing numerous challenges.
IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels
Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce.
Pruning by Active Attention Manipulation
On CIFAR-10 dataset, without requiring a pre-trained baseline network, we obtain 1. 02% and 1. 19% accuracy gain and 52. 3% and 54% parameters reduction, on ResNet56 and ResNet110, respectively.
Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data
Modern tourism in the 21st century is facing numerous challenges.
Entangled Residual Mappings
Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers.
End-to-End Sensitivity-Based Filter Pruning
Moreover, by training the pruning scores of all layers simultaneously our method can account for layer interdependencies, which is essential to find a performant sparse sub-network.
3D-OOCS: Learning Prostate Segmentation with Inductive Bias
Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different protocols utilized by different scanners, and to the variety of image properties or artefacts they produce.
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Robustness to variations in lighting conditions is a key objective for any deep vision system.
