Szczegóły publikacji
Opis bibliograficzny
Flattening layer pruning in convolutional neural networks / Ernest JĘCZMIONEK, Piotr A. KOWALSKI // Symmetry [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2073-8994. — 2021 — vol. 13 iss. 7 art. no. 1147, s. 1–13. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2021-06-27. — P. A. Kowalski – dod. afiliacja: Systems Research Institute, Polish Academy of Sciences
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134973 |
|---|---|
| Data dodania do BaDAP | 2021-06-29 |
| Tekst źródłowy | URL |
| DOI | 10.3390/sym13071147 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Symmetry |
Abstract
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.
