1 citations · 1 across the 9 of their papers we have counts for
9 papers
Accelerating Deep Learning with Fixed Time Budget
Muhammad Asif Khan, Ridha Hamila, Hamid Menouar
The success of modern deep learning is attributed to two key elements: huge amounts of training data and large model sizes. Where a vast amount of data allows the model to learn mo…
Multimodal Crowd Counting with Pix2Pix GANs
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Most state-of-the-art crowd counting methods use color (RGB) images to learn the density map of the crowd. However, these methods often struggle to achieve higher accuracy in dense…
Curriculum for Crowd Counting -- Is it Worthy?
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Recent advances in deep learning techniques have achieved remarkable performance in several computer vision problems. A notably intuitive technique called Curriculum Learning (CL)…
A Comprehensive Survey On Client Selections in Federated Learning
Ala Gouissem, Zina Chkirbene, Ridha Hamila
Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate…
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the q…
Visual Crowd Analysis: Open Research Problems
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep-learning approaches have made it…