Efficient Deep Learning Model Optimization for Resource Constrained Devices = Tõhus süvaõppe mudeli optimeerimine piiratud ressurssidega seadmete jaoks
author
supervisor
statement of authorship
Olutosin Ajibola Ademola ; [supervisors: Eduard Petlenkov, Mairo Leier ; Tallinn University of Technology, School of Information Technologies, Department of Computer Systems]
type of dissertation
doktoritöö
university/scientific institution
Tallinna Tehnikaülikool
location of publication
Tallinn
publisher
year of publication
pages
122 p. : ill
series
Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 37/2025
subject term
ISSN
2585-6898
2585-6901 (PDF)
ISBN
978-9916-80-319-6
978-9916-80-320-2 (PDF)
notes
Autori publikatsioonide nimekiri leheküljel 11
Bibliogr. lk. 74-79
Kokkuvõte eesti keeles
Kättesaadav ka võrguteavikuna
Autori CV inglise ja eesti keeles, lk. 121-122
Thesis (Ph.D. in Computer Systems Engineering) : Tallinn University of Technology, 2025
url
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
TalTech department
language
inglise
- Evaluation of deep neural network compression methods for edge devices using weighted score-based ranking scheme
- Ensemble of Tensor Train Decomposition and Quantization Methods for Deep Learning Model Compression
- Resource-aware scene text recognition using learned features, quantization, and contour-based character extraction
Ademola, O.A. Efficient Deep Learning Model Optimization for Resource Constrained Devices = Tõhus süvaõppe mudeli optimeerimine piiratud ressurssidega seadmete jaoks. Tallinn : TalTech Press, 2025. 122 p. : ill. (Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 37/2025).