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Deep Learning

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Deep Learning is a hierarchical learning methodology based on artificial neural networks which are algorithms inspired by the structure and function of the brain. It has applications in wide-range of industries these days such as face-recognisers working at massive scales, robotics, speech translation, text analysis, improving customer experience, autonomous vehicles etc. In this course we…

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M. Fakhir Khan

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M Fakhir Khan was a Research Assistant in Computer Vision Lab (cvlab) at LUMS Syed Babar Ali School of Science and Engineering. Research Interest M. Fakhir Khan received the B.S. degree in Mechatronics and Control Engineering from the University of Engineering and Technology, Lahore, Pakistan, in 2015, and the M.S. degree in Electrical Engineering from…

Arif Mahmood
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Arif Mahmood

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Arif Mahmood successfully defended his Ph.D. at Lahore University of Management Sciences in May 2011. Dr. Mahmood’s research interests broadly span the areas of image processing and computer vision. More specifically, he is interested in optimization of image processing algorithms from computational perspective. He worked on fast image matching techniques and developed new bound based…

ICPR2022 – Neural Network Pruning Through Constrained Reinforcement Learning
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ICPR2022 – Neural Network Pruning Through Constrained Reinforcement Learning

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Shehryar Malik, Muhammad Umair Haider*, Omer Iqbal, Murtaza Taj Abstract: Network pruning reduces the size of neural networks by removing (pruning) neurons such that the performance drop is minimal. Traditional pruning approaches focus on designing metrics to quantify the usefulness of a neuron which is often quite tedious and sub-optimal. More recent approaches have instead…

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ICIAP 2019 – Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval

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Mohbat, Tooba Mukhtar, Numan Khurshid, and Murtaza Taj International Conference on Image Analysis and Processing (ICAIP), Trento, Itlay, September 9-13, 2019 Abstract Advancements in deep learning techniques caused a paradigm shift in feature extraction for image perception from handcrafted methods to deep methods. However, these deep features if learned through unsupervised methods bear large memory…

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