4 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…