Data Augmentation for Time Series Classification using Convolutional Neural Networks

The good results obtained by the texture transfer learning using a deep architecture suggest that features learned by Convolutional Neural Networks used for image super-resolution

As opposed to classic neural networks techniques, which strongly depend on manually selected feature extraction methods, deep learning algorithms are able to extract their own

Keywords: Deep Learning, Faster R-CNN, Cell Counting, Machine Learning, Neural Networks, Computer Vision, Object Detection, Convolutional Neural Networks, Incep- tion ResNet

then it is a model trained with data from the camera placed inside the chamber with high resolution, with three convolutional layers, one dense layer and 256 nodes for each layer,

Feedforward neural networks (FNNs), or multilayer perceptrons (MLPs) [53], are the archetypes of deep learning models. These networks were inspired by neuroscience and how

The comparison of classification in artificial and fuzzy neural networks technique has been conducted to separate classes of forest from non-forest using SPOT satellite

The CNN architecture is parameterized by the number of layers, the number of maps per layer, the size of the kernel for the convolutional layer, the size of the subsampling window,

We search Dark Energy Survey (DES) Year 3 imaging for galaxy–galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets