DMCA
Learning with augmented class by exploiting unlabeled data (2014)
| Venue: | in Proceedings of the 28th AAAI Conference on Artificial Intelligence |
| Citations: | 5 - 1 self |
BibTeX
@INPROCEEDINGS{Da14learningwith,
author = {Qing Da and Yang Yu and Zhi-hua Zhou},
title = {Learning with augmented class by exploiting unlabeled data},
booktitle = {in Proceedings of the 28th AAAI Conference on Artificial Intelligence},
year = {2014}
}
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Abstract
In many real-world applications of learning, the envi-ronment is open and changes gradually, which requires the learning system to have the ability of detecting and adapting to the changes. Class-incremental learning (C-IL) is an important and practical problem where data from unseen augmented classes are fed, but has not been studied well in the past. In C-IL, the system should be-ware of predicting instances from augmented classes as a seen class, and thus faces the challenge that no such instances were observed during training stage. In this paper, we tackle the challenge by using unlabeled data, which can be cheaply collected in many real-world ap-plications. We propose the LACU framework as well as the LACU-SVM approach to learn the concept of seen classes while incorporating the structure presented in the unlabeled data, so that the misclassification risks among the seen classes as well as between the aug-mented and the seen classes are minimized simultane-ously. Experiments on diverse datasets show the effec-tiveness of the proposed approach.
