Transfer Learning with Active Queries from Source Domain
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1592-1597.
Abstract
To learn with limited labeled data, active learning tries to query more labels from an oracle, while transfer learning tries to utilize the labeled data from a related source domain. However, in many real cases, there is very few labeled data in both source and target domains, and the oracle is unavailable in the target domain. To solve this practical yet rarely studied problem, in this paper, we jointly perform transfer learning and active learning by querying the most valuable information from the source domain. The computation of importance weights for domain adaptation and the instance selection for active queries are integrated into one unified framework based on distribution matching, which is further solved with alternating optimization. The effectiveness of the proposed method is validated by experiments on 15 datasets for sentiment analysis and text categorization.
@inproceedings{ijcai2016p228,
title = {Transfer Learning with Active Queries from Source Domain},
author = {Huang, Sheng-Jun and Chen, Songcan},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {1592--1597},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/228},
}
TY - CPAPER TI - Transfer Learning with Active Queries from Source Domain AU - Huang, Sheng-Jun AU - Chen, Songcan T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 1592 EP - 1597 UR - https://ijcai.org/proceedings/2016/228 ER -Copied
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