343 citations · 805 across the 37 of their papers we have counts for
16 papers · 1 filter
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
Jiahua Dong, Yang Cong, Gan Sun +3
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most exist…
Generative Partial Visual-Tactile Fused Object Clustering
Tao Zhang, Yang Cong, Gan Sun +3
Visual-tactile fused sensing for object clustering has achieved significant progresses recently, since the involvement of tactile modality can effectively improve clustering perfor…
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation
Taotao Jing, Bingrong Xu, Jingjing Li +1
Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly…
Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation
Taotao Jing, Zhengming Ding
Unsupervised Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain. Conventio…
Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation
Taotao Jing, Haifeng Xia, Zhengming Ding
Partial domain adaptation (PDA) attracts appealing attention as it deals with a realistic and challenging problem when the source domain label space substitutes the target domain.…
Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation
Taotao Jing, Ming Shao, Zhengming Ding
Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing…
Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation
Shuang Li, Binhui Xie, Jiashu Wu +3
Heterogeneous domain adaptation (HDA) transfers knowledge across source and target domains that present heterogeneities e.g., distinct domain distributions and difference in featur…
Rethink Maximum Mean Discrepancy for Domain Adaptation
Wei Wang, Haojie Li, Zhengming Ding +1
Existing domain adaptation methods aim to reduce the distributional difference between the source and target domains and respect their specific discriminative information, by estab…
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Peizhao Li, Zhengming Ding, Hongfu Liu
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous efforts have been made to minim…
Domain Conditioned Adaptation Network
Shuang Li, Chi Harold Liu, Qiuxia Lin +4
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature r…
Sparsely-Labeled Source Assisted Domain Adaptation
Wei Wang, Zhihui Wang, Yuankai Xiang +4
Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available…
Maximum Density Divergence for Domain Adaptation
Li Jingjing, Chen Erpeng, Ding Zhengming +3
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctiv…
Deep Residual Correction Network for Partial Domain Adaptation
Shuang Li, Chi Harold Liu, Qiuxia Lin +4
Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled t…
Generative Partial Multi-View Clustering
Qianqian Wang, Zhengming Ding, Zhiqiang Tao +2
Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research att…
Towards Fair Cross-Domain Adaptation via Generative Learning
Tongxin Wang, Zhengming Ding, Wei Shao +2
Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. Existing DA normally…
Bi-Directional Generation for Unsupervised Domain Adaptation
Guanglei Yang, Haifeng Xia, Mingli Ding +1
Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domai…