Learning to Learn By Self-Critique

Antreas Antoniou; Amos J. Storkey

NeurIPS 2019poster93 citations

Learning to Learn By Self-Critique

Abstract

In few-shot learning, a machine learning system is required to learn from a small set of labelled examples of a specific task, such that it can achieve strong generalization on new unlabelled examples of the same task. Given the limited availability of labelled examples in such tasks, we need to make use of all the information we can. For this reason we propose the use of transductive meta-learning for few shot settings to obtain state-of-the-art few-shot learning.

BibTeX

@inproceedings{NEURIPS2019_6018df18,
 author = {Antoniou, Antreas and Storkey, Amos J},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Learning to Learn By Self-Critique},
 url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/6018df1842f7130f1b85a6f8e911b96b-Paper.pdf},
 volume = {32},
 year = {2019}
}