Aamir Mustafa
About Me
I am a PhD student at the Graphics & Interaction (Rainbow) Group, University of Cambridge, working under the supervision of Dr. Rafal K. Mantiuk. My current research is focused on Applications of Machine/Deep Learning in Computer Graphics.
LinkedIn GitHub Google Scholar Academic CV
Latest News
- 1 paper accepted in European Conference on Computer Vision (ECCV) 2020
- 1 paper accepted in IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 2020
- 1 paper accepted in IEEE Transactions on Image Processing (TIP) 2020
- 1 paper accepted in International Conference on Computer Vision (ICCV) 2019
Publications
Transformation Consistency Regularization - A Semi Supervised Paradigm for Image-to-Image Translation
European Conference on Computer Vision (ECCV), 2020
Project Page Paper Supplementary Material Code
Deeply Supervised Discriminative Learning for Adversarial Defense
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2020
Paper Code
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
International Conference on Computer Vision (ICCV), 2019
Paper Code
Image Super-Resolution as a Defense against Adversarial Attacks
IEEE Transactions on Image Processing (TIP), 2020
Paper Code
Experience
PhD Student - Computer Science Dept. University of Cambridge (Oct 2019 - Present)
Leveraging the use of semi-supervised learning for image to image translation.
Computer Vision Research Intern - Inception Institute of Artificial Intelligence (Sep 2018 - Sep 2019)
Working on making deep neural networks robust against adversarial attacks.
Computer Vision Research Intern - Indian Institute of Technology, Ropar (Dec 2017 - Mar 2018)
Worked on prediction and localization of student engagement in response to a stimuli video (e-learning environment) from facial expressions using Deep Multi-Instance Learning.
Machine Learning Research Intern - University of Canberra, Australia (Dec 2016 - Feb 2017)
Estimation of Heart rate of different individuals and its variations over the span of video from their facial videos by extracting plethysmograph (PG) signals from green channel of the frames. Considering heart rate as extracted feature, individuals are classified into two categories - healthy controls and depressed patients using a linear SVM classifier.
Current Projects
Transformation Consistency Regularization
Dehazing Experiments on Real Images
Image Super-Resolution using EDSR
Image Super-Resolution using EDSR on General100 Dataset
Image Super-Resolution using Scale Invaraint Discriminator Loss (EDSR) Trained on Different Images
Image Super-Resolution using SR Resnet Forest vs Stone
Image Super-Resolution using SR Resnet-- All Losses
Deep Image Prior Using SinGan Loss
Image Super-Resolution using EDSR Varying Alpha
Contact
Email: am2806@cam.ac.uk
Phone: +44 (0)1223 763706
Office: SE18, Computer Laboratory
Address: Computer Laboratory, William Gates Building, 15 JJ Thomson Ave, Cambridge CB3 0FD