32 citations · 55 across the 3 of their papers we have counts for
3 papers
cs.CV2024★ 32 cited
Inter-Domain Mixup for Semi-Supervised Domain Adaptation
Jichang Li, Guanbin Li, Yizhou Yu
Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available, achieving better classification perf…
cs.CV2024★ 21 cited
Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation
Jichang Li, Guanbin Li, Yizhou Yu
Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA) aims to significantly improve the classification performance and generalization capability of t…
cs.CV2023★ 2 cited
Divide and Adapt: Active Domain Adaptation via Customized Learning
Duojun Huang, Jichang Li, Weikai Chen +3
Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating active learning (AL) techniques to label a maximally-informative subset of target s…