most citedDual Adversarial Auto-Encoders for Clustering

59 citations · 125 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV202043 cited

Learning Target Domain Specific Classifier for Partial Domain Adaptation

Chuan-Xian Ren, Pengfei Ge, Peiyi Yang +1

Unsupervised domain adaptation~(UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous…

cs.CV202022 cited

Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification

Chuan-Xian Ren, Pengfei Ge, Dao-Qing Dai +1

Conditional Maximum Mean Discrepancy (CMMD) can capture the discrepancy between conditional distributions by drawing support from nonlinear kernel functions, thus it has been succe…

cs.CV202059 cited

Dual Adversarial Auto-Encoders for Clustering

Pengfei Ge, Chuan-Xian Ren, Jiashi Feng +1

As a powerful approach for exploratory data analysis, unsupervised clustering is a fundamental task in computer vision and pattern recognition. Many clustering algorithms have been…

cs.LG2020

Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment

You-Wei Luo, Chuan-Xian Ren, Pengfei Ge +2

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strate…

cs.CV20191 cited

Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation

Chuan-Xian Ren, Bo-Hua Liang, Zhen Lei

Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain…