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VeraWyrick

Neural networks have been extensively utilized for performing duties in critical domains, such as, for example, the medical area; their robustness is, therefore, necessary to be assured. In this paper, we suggest a robustness definition for neural networks used for regression, by tackling a few of the issues of existing robustness definitions. Initially, by following current works achieved for classification problems, we propose to outline the robustness of networks used for regression w.r.t. Since totally different alteration ranges usually are not all the time equally probable, the robustness definition is parameterized with the likelihood distribution of the alterations. The error performed by this kind of networks is quantifiable because the distinction between the estimated value and the expected worth; since not all the errors are equally essential, the robustness definition can be parameterized with a "tolerance" perform that specifies how the error is tolerated. The present work has been motivated by the collaboration with the industrial associate that has applied a medical sensor using a Multilayer Perceptron for the estimation of the blood oxygen stress. After having computed the robustness for the case research, we've got efficiently utilized three methods to enhance the network robustness: knowledge augmentation with recombined information, information augmentation with altered knowledge, and incremental learning. All the methods have proved to contribute to rising the robustness, though in other ways.

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