ICML 2017poster765 citations
Asymmetric Tri-training for Unsupervised Domain Adaptation
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
Abstract
It is important to apply models trained on a large number of labeled samples to different domains because collecting many labeled samples in various domains is expensive. To learn discriminative representations for the target domain, we assume that artificially labeling the target samples can result in a good representation. Tri-training leverages three classifiers equally to provide pseudo-labels to unlabeled samples; however, the method does not assume labeling samples generated from a different domain. In this paper, we propose the use of an
BibTeX
@InProceedings{pmlr-v70-saito17a,
title = {Asymmetric Tri-training for Unsupervised Domain Adaptation},
author = {Kuniaki Saito and Yoshitaka Ushiku and Tatsuya Harada},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {2988--2997},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/saito17a/saito17a.pdf},
url = {https://proceedings.mlr.press/v70/saito17a.html},
abstract = {It is important to apply models trained on a large number of labeled samples to different domains because collecting many labeled samples in various domains is expensive. To learn discriminative representations for the target domain, we assume that artificially labeling the target samples can result in a good representation. Tri-training leverages three classifiers equally to provide pseudo-labels to unlabeled samples; however, the method does not assume labeling samples generated from a different domain. In this paper, we propose the use of an