2.7k citations · 3.5k across the 17 of their papers we have counts for
17 papers
One-shot federated unsupervised domain adaptation with Smoothed Knowledge Distillation and teacher refinement
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated unsupervised domain adaptation (FUDA) addresses the challenge of adapting models to an unlabeled target domain using decentralized source domains while preserving data pr…
A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles
Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu +1
Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in co…
Few-Shot Inspired Generative Zero-Shot Learning
Md Shakil Ahamed Shohag, Q. M. Jonathan Wu, Farhad Pourpanah
Generative zero-shot learning (ZSL) methods typically synthesize visual features for unseen classes using predefined semantic attributes, followed by training a fully supervised cl…
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning. However, it faces significant challenges when dealing with domain shifts, especially…
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani +2
In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data hete…
Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data. Despite recent advance…
Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
Salim Rezvani, Farhad Pourpanah, Chee Peng Lim +1
This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants…
Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients
Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany +2
We address the problem of federated domain generalization in an unsupervised setting for the first time. We first theoretically establish a connection between domain shift and alig…
An Attentive-based Generative Model for Medical Image Synthesis
Jiayuan Wang, Q. M. Jonathan Wu, Farhad Pourpanah
Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and…
Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
Farhad Pourpanah, Ali Etemad
Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring.…
An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
Farhad Pourpanah, Chee Peng Lim, Ali Etemad +1
Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To ad…
A Review of Deep Learning for Video Captioning
Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi +8
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and…
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
Jiayuan Wang, Q. M. Jonathan Wu, Farhad Pourpanah
Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis…
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
Xinlei Zhou, Han Liu, Farhad Pourpanah +2
Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. Epistemic uncertainty, which usually is due to insufficient k…
A Review of Generalized Zero-Shot Learning Methods
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo +5
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To add…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
Farhad Pourpanah, Ran Wang, Chee Peng Lim +2
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a numb…