574 citations · 729 across the 6 of their papers we have counts for
10 papers
Network Architecture Search for Domain Adaptation
Yichen Li, Xingchao Peng
Deep networks have been used to learn transferable representations for domain adaptation. Existing deep domain adaptation methods systematically employ popular hand-crafted network…
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
Xingchao Peng, Yichen Li, Kate Saenko
Conventional unsupervised domain adaptation (UDA) studies the knowledge transfer between a limited number of domains. This neglects the more practical scenario where data are distr…
Learning Domain Adaptive Features with Unlabeled Domain Bridges
Yichen Li, Xingchao Peng
Conventional cross-domain image-to-image translation or unsupervised domain adaptation methods assume that the source domain and target domain are closely related. This neglects a…
Class-imbalanced Domain Adaptation: An Empirical Odyssey
Shuhan Tan, Xingchao Peng, Kate Saenko
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However, the current literature assumes that the label distribution is domain-invarian…
Domain Agnostic Learning with Disentangled Representations
Xingchao Peng, Zijun Huang, Ximeng Sun +1
Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of tar…
Moment Matching for Multi-Source Domain Adaptation
Xingchao Peng, Qinxun Bai, Xide Xia +3
Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are col…