IEEE

LHNet: Laplacian Convolutional Block for Remote Sensing Image Scene Classification

Abstract: Recently, many state-of-the-art results for remote sensing image scene classification have been achieved by convolutional neural networks (CNNs) due to their large learning capability.
Embedded

Understanding convolutional neural networks

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...
IEEE

Streaming Convolutional Neural Networks for End-to-End Learning With Multi-Megapixel Images

Abstract: Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain ...
Frontiers

Detection Method of Citrus Psyllids With Field High-Definition Camera Based on Improved Cascade Region-Based Convolution Neural Networks

Citrus psyllid is the only insect vector of citrus Huanglongbing (HLB), which is the most destructive disease in the citrus industry. There is no effective treatment for HLB, so detecting citrus ...
Frontiers

Accurate Tumor Segmentation via Octave Convolution Neural Network

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many ...
GitHub

SVM and CNN on Fashion MNIST Dataset

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a ...