153 citations · 276 across the 9 of their papers we have counts for
8 papers · 1 filter
Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models
Muxing Li, Zesheng Ye, Sharon Li +3
The proliferation of diffusion models trained on web-scale, provenance-uncertain image collections has made it essential, yet technically unresolved, to determine whether a model h…
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Li Zhong, Zhen Fang, Feng Liu +3
Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learn…
Learning from a Complementary-label Source Domain: Theory and Algorithms
Yiyang Zhang, Feng Liu, Zhen Fang +3
In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with massive true-label data from the source domain and unlabeled data from the target domain…
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
Yiyang Zhang, Feng Liu, Zhen Fang +3
In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with massive true-label data from the source domain and unlabeled data from the target domain…
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
Li Zhong, Zhen Fang, Feng Liu +3
In the unsupervised open set domain adaptation (UOSDA), the target domain contains unknown classes that are not observed in the source domain. Researchers in this area aim to train…
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Zhen Fang, Jie Lu, Feng Liu +2
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- t…
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Feng Liu, Jie Lu, Bo Han +3
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However,…
Heterogeneous domain adaptation: An unsupervised approach
Feng Liu, Guanquan Zhang, Jie Lu
Domain adaptation leverages the knowledge in one domain - the source domain - to improve learning efficiency in another domain - the target domain. Existing heterogeneous domain ad…