Efficient Deep Learning Architectures for Computer Vision Deployment on Industrial Scenarios
Viera, Jose Renato Restom
Viera, Jose Renato Restom
Author
Supervisor
Department
Computer Vision
Embargo End Date
Type
Thesis
Date
2023
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Language
English
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Abstract
This work will explore the potential of efficient deep learning architectures for computer vision tasks and their suitability for federated learning. The primary focus will be on designing, implementing, and evaluating efficient deep learning architectures for computer vision tasks that can make precise predictions in real time while optimizing computational resources. The findings of this study will advance the current understanding of efficient deep learning architectures for computer vision and their potential for privacy-preserving federated learning.
Citation
J.R.R. Viera, "Efficient Deep Learning Architectures for Computer Vision Deployment on Industrial Scenarios", M.S. Thesis, Computer Vision, MBZUAI, Abu Dhabi, UAE, 2023.