150 citations · 335 across the 3 of their papers we have counts for
3 papers
cs.CV2019★ 150 cited
Federated Adversarial Domain Adaptation
Xingchao Peng, Zijun Huang, Yizhe Zhu +1
Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Ye…
cs.CV2019★ 138 cited
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…
cs.CV2018★ 47 cited
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…