M. Ahmed Bhimra
M. Ahmed Bhimra is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
M. Ahmed Bhimra is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Talha Hanif Butt is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
Usman Nazir, Numan Khurshid, Muhammad Ahmed Bhimra, Murtaza Taj International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16-21, 2019 Abstract This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within “Brick-Kiln-Belt” of South Asia. The framework is developed…
M. Wadood Islam was an MS-CS student at LUMS and a Graduate Research Assistant at Computer Vision and Graphics Lab (CVGL), LUMS Syed Babar Ali School of Science and Engineering. He earned his undergraduate degree from FAST-NUCES in Computer Science. His research interests include machine learning, deep learning, computer vision, and bioinformatics. He worked on classifying…
Kashif Murtaza is a Ph.D. student in the Department of Computer Science, Lahore University of Management Sciences. Publication: BMVC 2009 it fall at least as much as spring or summer youjizz Nike SB Dunk High Premium Wool Cliff Huxtable Our men’s wholesale sales grew 44 anime pornto support Houston fashion designer Lynx WOMEN’S SYNTHETIC GOLF…
Research Associate | Full Stack Developer | Web Scraping and Automation Expert | Certified DL & ML Specialist Muhammad Muqsit Islam is a full-stack developer proficient in Web Development, Computer Vision, and Deep Learning. They are skilled in Django, Django Rest Framework, React, Flask, TensorFlow, Docker and other technologies, effectively bridging the gap between frontend…
Shaiq Munir Malik, Fnu Mohbat, Muhammad Umair Haider, Muhammad Musab Rasheed and Murtaza Taj Abstract: To reduce the overwhelming size of Deep Neural Networks, teacher-student techniques aim to transfer knowledge from a complex teacher network to a simple student network. We instead propose a novel method called the teacher-class network consisting of a single teacher…