Toward a Deep Neural Approach for Knowledge-Based IR

Other aberration orders will be corrected with the same method, to improve lateral resolution up to the diffraction limit in digital holo- graphic imaging of the retina.The

For each phase in the process model, we discuss its objectives to be achieved and the methods and metrics (M&M-s) that can be used to ensure higher trustworthiness..

In this paper, we propose natural attacks using visibly foggy images to generate input that causes incorrect classi- fication by the Inception deep learning model [22].. Apart from

The deep neural network with an input layer, a set of hidden layers and an output layer can be used for further considerations.. As standard ReLu activation function is used for all

The percentages of misclassified examples, presented in Tables 7 8, showed that even with a much smaller training dataset, with four or five base models the architecture

[34] S´ebastien Razakarivony and Fr´ed´eric Jurie, “Vehicle Detection in Aerial Imagery: A small target detection benchmark,” Jour- nal of Visual Communication and

This work focusses on developing hard-mining strategies to deal with classes with few examples and is a lot faster than all former approaches due to the small sized network and

Inspired by Random Neural Networks, we introduce Random Neural Layer (RNL), which is compatible with other kinds of neural layers in Deep Neural Networks (DNN). The