Master Thesis Online
Master Thesis : Semi-Supervised Learning For Computer Vision
Makedonsky, Aliocha 
Promotor(s) :
Geurts, Pierre
;
Nath Ranga, Rakesh
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/17721
Details
| Title : | Master Thesis : Semi-Supervised Learning For Computer Vision |
| Translated title : | [fr] Apprentissage semi-supervisé pour vision par ordinateur |
| Author : | Makedonsky, Aliocha
|
| Date of defense : | 26-Jun-2023/27-Jun-2023 |
| Advisor(s) : | Geurts, Pierre
Nath Ranga, Rakesh
|
| Committee's member(s) : | Louppe, Gilles
Wehenkel, Louis
|
| Language : | English |
| Number of pages : | 85 |
| Keywords : | [en] semi-supervised learning [en] machine learning [en] computer vision [en] imbalance |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Target public : | Professionals of domain Student |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master : ingénieur civil en science des données, à finalité spécialisée |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] When working on real-world projects, the dataset might contain few labeled samples and much more unlabeled samples. The purpose of this thesis is to explore techniques that would leverage unlabeled data to improve the results of a model that would have been trained only in the few labeled samples.
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