prikaz prve stranice dokumenta Optimiranje konvolucijskih modela iterativnim podrezivanjem
No public access
master's thesis
Optimiranje konvolucijskih modela iterativnim podrezivanjem
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:003963
Močilac, Stjepan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Močilac, S. (2020). Optimiranje konvolucijskih modela iterativnim podrezivanjem (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:003963

Močilac, Stjepan. "Optimiranje konvolucijskih modela iterativnim podrezivanjem." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:003963

Močilac, Stjepan. "Optimiranje konvolucijskih modela iterativnim podrezivanjem." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:003963

Močilac, S. (2020). 'Optimiranje konvolucijskih modela iterativnim podrezivanjem', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 03 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:003963

Močilac S. Optimiranje konvolucijskih modela iterativnim podrezivanjem [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 November 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:003963

S. Močilac, "Optimiranje konvolucijskih modela iterativnim podrezivanjem", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:003963

Metadata
TitleOptimiranje konvolucijskih modela iterativnim podrezivanjem
Title (english)Optimizing convolutional models by iterative pruning
AuthorStjepan Močilac
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberVladimir Čeperić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad se bavi pronalažanjem podmreža malog kapaciteta i dobre generalizacije unutar većeg dubokog modela. Pretraživanje se obavlja postupkom strukturnog podrezivanja i podrezivanja težina. Eksperimenti su rađeni nad VGG-19 i ResNet-18 arhitekturama. Pokazuje se da postoji podmreža mreže ResNet-18 koja nad CIFAR-10 skupom za učenje postiže 1.37\% veću točnost i ima 20\% parametara originalnog modela. Uz to, takva podmreža postiže 2.5 puta bržu evaluaciju na GPU uređaju od originalne mreže.
Abstract (english)
The goal of this thesis is to find small capacity and good generalization subnet within a larger deep network. The search is performed by the process of structural and weight pruning. Experiments were done using VGG-19 and ResNet-18 architectures. It is shown that there is a sub-network of the ResNet-18 network that achieves 1.37 \% higher accuracy on the CIFAR-10 dataset and has 20 \% parameters of the original model. Also, it achieves 2.5 times faster evaluations on the GPU device than the original network.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:003963
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2023-01-13 10:29:27