39 citations · 115 across the 11 of their papers we have counts for
12 papers · 1 filter
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
Jingru Yi, Pengxiang Wu, Hui Tang +7
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells…
Vicinal and categorical domain adaptation
Hui Tang, Kui Jia
Unsupervised domain adaptation aims to learn a task classifier that performs well on the unlabeled target domain, by utilizing the labeled source domain. Inspiring results have bee…
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
Hui Tang, Xiatian Zhu, Ke Chen +2
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose dist…
Partly Supervised Multitask Learning
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model…
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
Hui Tang, Ke Chen, Kui Jia
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target o…
Discriminative Adversarial Domain Adaptation
Hui Tang, Kui Jia
Given labeled instances on a source domain and unlabeled ones on a target domain, unsupervised domain adaptation aims to learn a task classifier that can well classify target insta…