1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation
Yongguang Li, Yueqi Cao, Jindong Li +2
Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model's performance from a labeled source domain to an unlabeled target domain without direct access to sourc…
cs.LG2024
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos
Tianyi Zhang, Yu Cao, Dianbo Liu
Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos. While previous studies have…
cs.CV2015★ 1 cited
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
Yuewei Lin, Jing Chen, Yu Cao +4
This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all…