Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest remote sensing community, who sees it as an opportunity to compensate for satellites’ ever-limited spatial resolution with respect end users’ needs. This is especially true Sentinel-2 because its unique combination resolution, revisit time, global coverage and free open data policy. While...

On high dimensional data sets choosing subspaces randomly, as in RASCO (Random Subspace Method for Co-training, Wang et al. 2008) algorithm, may produce diverse but inaccurate classifiers for Co-training. In order to remedy this problem, we introduce two algorithms for selecting relevant and non-redundant feature subspaces for Co-training. First algorithm relevant random subspaces (Rel-RASCO) p...

This article focuses on gradient-based backpropagation algorithms that use either a common adaptive learning rate for all weights or an individual adaptive learning rate for each weight and apply the Goldstein/Armijo line search. The learning-rate adaptation is based on descent techniques and estimates of the local Lipschitz constant that are obtained without additional error function and gradi...

Abstract: A paper deals with application of stochastic methods for dynamic neural network training. The considered network is composed of dynamic neurons, which contain inner feedbacks. This network can be used as a part of a fault diagnosis system to generate residuals. Up-to-date training algorithms, based on the classical back propagation, suffer from entrapment in local minima of an error f...