Evaluation of Self-Supervised Learning for Vehicle-Type Detection in Autonomous Driving – Manaf AHMED
Manaf AHMED
Master's thesis
Evaluation of Self-Supervised Learning for Vehicle-Type Detection in Autonomous Driving
Abstract:
Self-Supervised Learning (SSL) method was implemented on vehicle-type detection task within autonomous driving context. The task involved evaluation of prominent SSL methods such as SimCLR and SimSiam against the conventional supervised method. The study explores the influence of different factors on the performance of SSL and its ability to generalize well under different circumstances.Abstract:
Self-Supervised Learning (SSL) method was implemented on vehicle-type detection task within autonomous driving context. The task involved evaluation of prominent SSL methods such as SimCLR and SimSiam against the conventional supervised method. The study explores the influence of different factors on the performance of SSL and its ability to generalize well under different circumstances.
Language used: Czech
Date on which the thesis was submitted / produced: 31. 8. 2023
Thesis defence
- Supervisor: prof. Dr. Patrick Glauner
Citation record
ISO 690-compliant citation record:
AHMED, Manaf. \textit{Evaluation of Self-Supervised Learning for Vehicle-Type Detection in Autonomous Driving}. Online. Master's thesis. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Science. 2023. Available from: https://theses.cz/id/3b4ycw/.
The right form of listing the thesis as a source quoted
AHMED, Manaf. Evaluation of Self-Supervised Learning for Vehicle-Type Detection in Autonomous Driving. České Budějovice, 2023. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Full text of thesis
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Published in Theses:- světu
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Institution archiving the thesis and making it accessible: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakultaUNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceMaster programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science
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