A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification
Abstract: Convolutional neural networks (CNNs) have been extensively studied for hyperspectral image classification (HSIC). However, CNNs are critically attributed to a large number of labeled ...
AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...
Researchers at the UCLA Samueli School of Engineering and CNSI (California NanoSystems Institute), led by Professor Aydogan ...
Recent years have witnessed great advances in applying deep learning to improve fluorescence microscopy imaging. However, enhancing the fidelity of image restoration networks and improving their ...
Subcortical ischemic vascular disease (SIVD), driven by cerebral small vessel disease, is commonly characterized by white matter hyperintensities and multiple lacunar infarcts, and a substantial ...
Researchers developed a hybrid UMAP-HDBSCAN-SVM machine learning workflow to rapidly classify low-loss STEM-EELS spectrum ...
[Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017. A curated list of the most cited deep learning papers ...
Abstract: The deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its powerful feature ...
This project explores image classification on the CIFAR-10 dataset using Convolutional Neural Networks (CNN) in MATLAB. It includes several neural network architectures implemented via MATLAB Live ...
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