Papers Multi-Source Unsupervised Domain Adaptation

“Multi-Source Unsupervised Domain Adaptation” 태그가 달린 논문 46편 · 필터 해제

Moment Matching for Multi-Source Domain Adaptation

2018-12-04 · ICCV 2019 10 · Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang 외

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 collected from multiple sources, requiring …

Benchmarking Domain Adaptation Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Adversarial Multiple Source Domain Adaptation

2018-12-01 · NeurIPS 2018 12 · Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura 외

While domain adaptation has been actively researched, most algorithms focus on the single-source-single-target adaptation setting. In this paper we propose new generalization bounds and algorithms under both classificati…

Classification Domain Adaptation General Classification Generalization Bounds+3

Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift

2018-03-02 · CVPR 2018 6 · Ruijia Xu, Ziliang Chen, WangMeng Zuo, Junjie Yan 외

Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data are typically collected from diverse sou…

Domain Adaptation Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

2017-12-07 · CVPR 2018 6 · Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, Tatsuya Harada

In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature genera…

Domain Adaptation image-classification Image Classification Multi-Source Unsupervised Domain Adaptation+2

Deep Transfer Learning with Joint Adaptation Networks

2016-05-21 · ICML 2017 8 · Mingsheng Long, Han Zhu, Jian-Min Wang, Michael. I. Jordan

Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a …

Multi-Source Unsupervised Domain Adaptation Transfer Learning

Learning Transferable Features with Deep Adaptation Networks

2015-02-10 · Mingsheng Long, Yue Cao, Jian-Min Wang, Michael. I. Jordan

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along …

Domain Adaptation image-classification Image Classification Multi-Source Unsupervised Domain Adaptation