18 citations · 45 across the 7 of their papers we have counts for
9 papers
GMML is All you Need
Sara Atito, Muhammad Awais, Josef Kittler
Vision transformers have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply co…
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Muhammad Awais, Fengwei Zhou, Chuanlong Xie +3
Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is…
Adversarial Robustness for Unsupervised Domain Adaptation
Muhammad Awais, Fengwei Zhou, Hang Xu +4
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled…
NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition
Syed Safwan Khalid, Muhammad Awais, Chi-Ho Chan +4
Face recognition (FR) using deep convolutional neural networks (DCNNs) has seen remarkable success in recent years. One key ingredient of DCNN-based FR is the appropriate design of…
A Flatter Loss for Bias Mitigation in Cross-dataset Facial Age Estimation
Ali Akbari, Muhammad Awais, Zhen-Hua Feng +2
The most existing studies in the facial age estimation assume training and test images are captured under similar shooting conditions. However, this is rarely valid in real-world a…
Towards an Adversarially Robust Normalization Approach
Muhammad Awais, Fahad Shamshad, Sung-Ho Bae
Batch Normalization (BatchNorm) is effective for improving the performance and accelerating the training of deep neural networks. However, it has also shown to be a cause of advers…