no code implementations • ECCV 2020 • Aamir Mustafa, Rafal K. Mantiuk
Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples.
1 code implementation • 30 Sep 2022 • Aamir Mustafa, Param Hanji, Rafal K. Mantiuk
Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each representing a different style.
no code implementations • 26 Mar 2021 • Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu, Varun Babbar, Rafal K. Mantiuk
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution.
1 code implementation • 15 Jul 2020 • Aamir Mustafa, Rafal K. Mantiuk
Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples.
1 code implementation • ICCV 2019 • Aamir Mustafa, Salman Khan, Munawar Hayat, Roland Goecke, Jianbing Shen, Ling Shao
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Ranked #7 on Adversarial Defense on CIFAR-10
1 code implementation • 7 Jan 2019 • Aamir Mustafa, Salman H. Khan, Munawar Hayat, Jianbing Shen, Ling Shao
The proposed scheme is simple and has the following advantages: (1) it does not require any model training or parameter optimization, (2) it complements other existing defense mechanisms, (3) it is agnostic to the attacked model and attack type and (4) it provides superior performance across all popular attack algorithms.
1 code implementation • 3 Apr 2018 • Amanjot Kaur, Aamir Mustafa, Love Mehta, Abhinav Dhall
Recognizing the lack of any publicly available dataset in the domain of user engagement, a new `in the wild' dataset is created to study the subject engagement problem.