25 citations · 70 across the 6 of their papers we have counts for
6 papers
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.…
Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer
Xiaodong Wang, Junbao Zhuo, Shuhao Cui +1
Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled targe…
Heuristic Domain Adaptation
Shuhao Cui, Xuan Jin, Shuhui Wang +2
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different…
Gradually Vanishing Bridge for Adversarial Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered t…
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary w…
Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization
Junbao Zhuo, Shuhui Wang, Shuhao Cui +1
We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is…