Active Zero-Shot Learning

Sihong Xie, Shaoxiong Wang, Philip S. Yu
2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16  
In multi-label classification in the big data age, the number of classes can be in thousands, and obtaining sufficient training data for each class is infeasible. Zero-shot learning aims at predicting a large number of unseen classes using only labeled data from a small set of classes and external knowledge about class relations. However, previous zero-shot learning models passively accept labeled data collected beforehand, relinquishing the opportunity to select the proper set of classes to
more » ... uire labeled data and optimize the performance of unseen class prediction. To resolve this issue, we propose an active class selection strategy to intelligently query labeled data for a parsimonious set of informative classes. We demonstrate two desirable probabilistic properties of the proposed method that can facilitate unseen classes prediction. Experiments on 4 text datasets demonstrate that the active zero-shot learning algorithm is superior to a wide spectrum of baselines. We indicate promising future directions at the end of this paper.
doi:10.1145/2983323.2983866 dblp:conf/cikm/XieWY16 fatcat:d5tvdsvqy5er5nnhitlxjomase