35 citations · 224 across the 26 of their papers we have counts for
26 papers
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Jian Xu, Xinyi Tong, Shao-Lun Huang
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One…
An Information-Theoretic Approach to Transferability in Task Transfer Learning
Yajie Bao, Yang Li, Shao-Lun Huang +4
Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks. An important question i…
Revisiting Sparse Convolutional Model for Visual Recognition
Xili Dai, Mingyang Li, Pengyuan Zhai +6
Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sp…
Transferability-Guided Cross-Domain Cross-Task Transfer Learning
Yang Tan, Enming Zhang, Yang Li +2
We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model…
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Jingge Wang, Liyan Xie, Yao Xie +2
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we con…
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Runpeng Yu, Hong Zhu, Kaican Li +5
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms…