207 citations · 466 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conv…
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…
Analysis of Scoliosis From Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Spi…
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…
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
Yabin Zhang, Bin Deng, Hui Tang +2
In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies a few recent algorithms whose learning objectives are only mo…