61 citations · 107 across the 5 of their papers we have counts for
6 papers
Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
Ying Lu, Liming Chen, Alexandre Saidi +1
Correctly estimating the discrepancy between two data distributions has always been an important task in Machine Learning. Recently, Cuturi proposed the Sinkhorn distance which mak…
Discriminative and Geometry Aware Unsupervised Domain Adaptation
Lingkun Luo, Liming Chen, Shiqiang Hu +2
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation o…
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Wuming Zhang, Zhixin Shu, Dimitris Samaras +1
Heterogeneous face recognition between color image and depth image is a much desired capacity for real world applications where shape information is looked upon as merely involved…
Optimal Transport for Deep Joint Transfer Learning
Ying Lu, Liming Chen, Alexandre Saidi
Training a Deep Neural Network (DNN) from scratch requires a large amount of labeled data. For a classification task where only small amount of training data is available, a common…
von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
Md. Abul Hasnat, Julien Bohné, Jonathan Milgram +2
A number of pattern recognition tasks, \textit{e.g.}, face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distanc…
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation
Lingkun Luo, Xiaofang Wang, Shiqiang Hu +1
Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unl…