Efficient Deep Learning Architectures for Computer Vision Deployment on Industrial Scenarios

Authors

Jose Renato Restom Viera, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Dissertation

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.

Publication Date

6-2023

Comments

Thesis submitted to the Deanship of Graduate and Postdoctoral Studies

In partial fulfillment of the requirements for the M.Sc degree in Computer Vision

Advisors: Dr. Hisham Cholakkal, Dr. Rao Anwer

with 1 year embargo period

Recommended 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.