Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
Open Science Slovenia
Open Science
DiKUL
slv
|
eng
Search
Browse
New in RUL
About RUL
In numbers
Help
Sign in
Destilacija znanja globokih modelov za biometrijo beločnice
ID
Bizjak, Matic
(
Author
),
ID
Peer, Peter
(
Mentor
)
More about this mentor...
,
ID
Štruc, Vitomir
(
Comentor
),
ID
Vitek, Matej
(
Comentor
)
PDF - Presentation file,
Download
(21,08 MB)
MD5: 011BC553CB21B2BCECB71D3E632A8CB4
Image galllery
Abstract
Destilacija znanja je pristop izdelave lahkih modelov s prenosom znanja iz globokih modelov, ki imajo veliko število parametrov, so časovno zahtevni in imajo zelo visoko natančnost. V magistrskem delu ovrednotimo pristop destilacije znanja na področju biometrije očesa. Izdelamo nov postopek pridobitve lahkega modela za segmentacijo beločnice s kombinacijo dveh pristopov, destilacije znanja in rezanja filtrov, ter pokažemo, da sta oba pristopa ključna za uspeh našega postopka. S predstavljenim izvirnim postopkom pridobitve lahkega modela odstranimo 74 % operacij s plavajočo vejico za eno sklepanje in 73,2 % parametrov ter izgubimo 1,27 % natančnosti, poleg tega pa odstranimo 2-krat toliko parametrov kot najsodobnejši model in v primerjavi izgubimo le 1,74 % natančnosti. V luči te primerjave na koncu identificiramo možne nadgradnje, ki imajo potencial za izboljšanje našega pristopa.
Language:
Slovenian
Keywords:
destilacija znanja
,
rezanje filtrov
,
konvolucijske nevronse mreže
,
beločnica
,
segmentacija
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FRI - Faculty of Computer and Information Science
Year:
2020
PID:
20.500.12556/RUL-121899
COBISS.SI-ID:
37218051
Publication date in RUL:
06.11.2020
Views:
1105
Downloads:
179
Metadata:
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Copy citation
Share:
Secondary language
Language:
English
Title:
Knowledge distillation of deep learning models for sclera biometrics
Abstract:
Knowledge distillation is a technique for the development of lightweight models by transferring knowledge from a deep model with high memory footprint and high computational complexity. In this work we evaluate knowledge distillation for eye biometrics. We propose a new algorithm for creating a lightweight model for sclera segmentation by combining knowledge distillation with filter pruning and show that both techniques are key to achieving good results. With the presented algorithm we remove 74% floating point operations needed for one inference and 73.2% parameters and sacrifice 1.27% of the accuracy. In addition, we remove twice as many parameters as the current state-of-the-art filter pruning approach and in comparison sacrifice 1.74% of the accuracy. In the light of this comparison, we identify possible improvements that have a potential to further increase the accuracy of our algorithm.
Keywords:
knowledge distillation
,
filter pruning
,
convolutional neural networks
,
sclera
,
segmentation
Similar documents
Similar works from RUL:
Similar works from other Slovenian collections:
Back