no code implementations • ICCV 2023 • Mateusz Michalkiewicz, Masoud Faraki, Xiang Yu, Manmohan Chandraker, Mahsa Baktashmotlagh
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks.
no code implementations • CVPR 2022 • Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter, Xiang Yu, Masoud Faraki, Amit K. Roy-Chowdhury, Manmohan Chandraker
In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better.
no code implementations • CVPR 2022 • Christian Simon, Masoud Faraki, Yi-Hsuan Tsai, Xiang Yu, Samuel Schulter, Yumin Suh, Mehrtash Harandi, Manmohan Chandraker
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task.
no code implementations • 28 Feb 2022 • Dongwan Kim, Yi-Hsuan Tsai, Yumin Suh, Masoud Faraki, Sparsh Garg, Manmohan Chandraker, Bohyung Han
First, a gradient conflict in training due to mismatched label spaces is identified and a class-independent binary cross-entropy loss is proposed to alleviate such label conflicts.
no code implementations • CVPR 2022 • Chang Liu, Xiang Yu, Yi-Hsuan Tsai, Ramin Moslemi, Masoud Faraki, Manmohan Chandraker, Yun Fu
Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.
no code implementations • CVPR 2021 • Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai, Yumin Suh, Manmohan Chandraker
Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains.
no code implementations • 9 Oct 2020 • Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker, Yu-Xiang Wang
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
no code implementations • ICLR 2019 • Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann
To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace.
no code implementations • CVPR 2015 • Masoud Faraki, Mehrtash T. Harandi, Fatih Porikli
This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds.
no code implementations • 9 Jun 2014 • Masoud Faraki, Maziar Palhang, Conrad Sanderson
Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions.