19 citations · 71 across the 10 of their papers we have counts for
10 papers
Global Variational Inference Enhanced Robust Domain Adaptation
Lingkun Luo, Shiqiang Hu, Liming Chen
Deep learning-based domain adaptation (DA) methods have shown strong performance by learning transferable representations. However, their reliance on mini-batch training limits glo…
Noise Optimized Conditional Diffusion for Domain Adaptation
Lingkun Luo, Shiqiang Hu, Liming Chen
Pseudo-labeling is a cornerstone of Unsupervised Domain Adaptation (UDA), yet the scarcity of High-Confidence Pseudo-Labeled Target Domain Samples (\textbf{hcpl-tds}) often leads t…
Decision Boundary Optimization-Informed Domain Adaptation
Lingkun Luo, Shiqiang Hu, Jie Yang +1
Maximum Mean Discrepancy (MMD) is widely used in a number of domain adaptation (DA) methods and shows its effectiveness in aligning data distributions across domains. However, in p…
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation
Lingkun Luo, Shiqiang Hu, Liming Chen
In domain adaptation (DA), the effectiveness of deep learning-based models is often constrained by batch learning strategies that fail to fully apprehend the global statistical and…
Attention Regularized Laplace Graph for Domain Adaptation
Lingkun Luo, Liming Chen, Shiqiang Hu
In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA methods have shown their effectiveness in preserving data manifold through the Laplace graph. Ho…
Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation
Lingkun Luo, Liming Chen, Shiqiang Hu
Domain adaptation (DA) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of data distributions between the tw…