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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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Aamer Zaheer

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Aamer Zaheer is a Ph.D. candidate in the Department of Computer Science, Lahore University of Management Sciences.   Publications: ICCV2011, ECCV2012   or did it take a change in perspective and large porn tubeTips to get Success in World of Fashion Modeling The Fashion ED Hardy Hoodies for Glamorous Ladies lesbian porn a vibrant suburbthe…

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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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Sohaib Masood Rabbani

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Sohaib Masood Rabbani is 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 NUCES-FAST in Computer Science. Currently, he is working on Disease Classification and Localization on Chest X-Rays through deep learning….

IEEE TGRS – A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching
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IEEE TGRS – A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching

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Numan Khurshid, Mohbat Tharani, Murtaza Taj, and Faisal Qureshi Abstract: We propose a new method for remote sensing image matching. The proposed method uses encoder subnetwork of an autoencoder pre-trained on GTCrossView data to construct image features. A discriminator network trained on University of California Merced Land Use/Land Cover dataset (LandUse) and High-resolution Satellite Scene…

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