Ahmed M. Yosri
Papers
1
Total Citations
11
H-Index
1
About
Ahmed M. Yosri is a researcher whose work sits at the intersection of deep learning, computer vision, and intelligent transportation systems. His primary focus is on solving the critical bottleneck of data scarcity in training large neural networks, particularly for autonomous driving and urban mobility applications. His most cited work, "A hybrid Cycle GAN-based lightweight road perception pipeline for road dataset generation for Urban mobility" (2023, 11 citations), introduces a novel generative framework that dramatically reduces the need for manual dataset labeling. By leveraging a hybrid CycleGAN architecture, Yosri’s pipeline can automatically produce high-fidelity, labeled road scene datasets, addressing one of the most labor-intensive challenges in deep learning. This contribution is especially impactful for researchers and engineers developing perception systems for self-driving cars, where massive, diverse datasets are essential but prohibitively expensive to create. Yosri’s work demonstrates a practical path toward scalable, automated data generation, making him a notable figure in the push for more efficient and accessible AI training methodologies in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1