|

Summer Internships 2018

By

The Computer Vision Lab hosted a rigorous summer internship program for undergraduate students. Sophomore, Junior and Senior interns worked for 2 months in the lab, under the supervision of faculty and PhD students. The students worked both individually and in groups, on a range of ideas, from making a campus 3D model to automatic generation of Mughal art motifs.

Tutorials:

Day 1: Exploratory Data Analysis and Feature Engineering by Usman Nazir

Library: Pandas

Case Study: Churn Prediction

Download: Day 1

Day 2: Chapter 2: Linear Algebra & Chapter 4: Numerical Computation  of Deep Learning Book by Usman Nazir and Faisal Yaseen

Lecture Slides Links: Chapter 2 & Chapter 4

Day 3: Hands-on training session on Optimization & Chapter 3: Probability and Information Theory of Deep Learning Book by Usman Nazir

Lecture Slide Link: Chapter 3

Download: gradient_descent-master

Day 4: Deep learning implementation for image recognition, object detection, and image segmentation by Numan Khurshid

Links: MNIST Visualization

Download:

Day 5: Autoencoders and Adversarial Autoencoders by Numan Khurshid

Reading Links: Adversarial Autoencoders, A wizard’s guide to Autoencoder

Video Links: Deep Learning Simplified, Adversarial Examples

Download: 5. GANs and Autoencoders

 

Similar Posts

5xMS Thesis Proposal Defence

By

Tuberculosis Diagnosis in Medical Imagery using Deep Learning by Inam Ullah Taj Estimating labor population in Brick Kilns using Satellite Imagery and Remote Sensing by Muhammad Awais Ather Compressing neural networks via teacher assisted learning by Umair Haider Online structured filter pruning for neural networks by Shaiq Munir Malik Semantic Segmentation of water channel to…

ICPR2022 – Neural Network Pruning Through Constrained Reinforcement Learning
|

ICPR2022 – Neural Network Pruning Through Constrained Reinforcement Learning

By

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…

|

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

By

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…

|

Summer Internships 2019

By

Content Based Image Retrieval Using Hand Crafted Features Asim Waheed, Khawaja Umair Ul Hassan The project involved solving the Cross-View image matching problem between Satellite view images and Street view images. Many hand-crafted features were calculated, such as Histogram, HOG, Bag of Visual Words and VLAD using SIFT and SURF descriptors. The compiled features would…

ICASSP2022 -Camera Calibration through Camera Projection Loss
|

ICASSP2022 -Camera Calibration through Camera Projection Loss

By

Talha Hanif Butt, Murtaza Taj Abstract: Camera calibration is a necessity in various tasks including 3D reconstruction, hand-eye coordination for a robotic interaction, autonomous driving, etc. In this work we propose a novel method to predict extrinsic (baseline, pitch, and translation), intrinsic (focal length and principal point offset) parameters using an image pair. Unlike existing…

Leave a Reply

Your email address will not be published. Required fields are marked *