The rapid growth of performance in the field of neural networks has also increased their sizes. Pruning methods are getting more and more attention in order to overcome the problem of non-impactful parameters and overgrowth of neurons. In this article, the application of Global Sensitivity Analysis (GSA) methods demonstrates the impact of input variables on the model’s output variables. GSA gives the ability to mark out the least meaningful arguments and build reduction algorithms on these. Using several popular datasets, the study shows how different levels of pruning correlate to network accuracy and how levels of reduction negligibly impact accuracy. In doing so, pre- and post-reduction sizes of neural networks are compared. This paper shows how Sobol and FAST methods with common norms can largely decrease the size of a network, while keeping accuracy relatively high. On the basis of the obtained results, it is possible to create a thesis about the asymmetry between the elements removed from the network topology and the quality of the neural network.

Transfer learning has surfaced as a compelling technique in machine learning, enabling the transfer of knowledge across networks. This study evaluates the...

The aim of this paper is to propose a new image data set for assessing the quality of solutions to machine learning tasks, in particular, deep neural networks. The data set is derived from X-ray images of wheat grains, in which three species, Kama, Rosa, and Canadian, are distinguished. In this paper, the structure of the data is presented in detail and ten pretrained deep neural networks are applied to identify individual wheat species. The Seeds Image Data Set, due to its compact nature, can compete with well-known and quite frequently used object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet, etc.). The compactness of the data set is based on a relatively small number of data instances, which shortens the rather time-consuming computing process. The proposed data set will be made available in a public repository, and the results presented will provide a starting point for other competing solutions for exploratory data analysis in the broad sense.