Learning to Compare: Relation Network for Few-Shot Learning
Abstract
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks.
Cite
Text
Sung et al. "Learning to Compare: Relation Network for Few-Shot Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00131Markdown
[Sung et al. "Learning to Compare: Relation Network for Few-Shot Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/sung2018cvpr-learning/) doi:10.1109/CVPR.2018.00131BibTeX
@inproceedings{sung2018cvpr-learning,
title = {{Learning to Compare: Relation Network for Few-Shot Learning}},
author = {Sung, Flood and Yang, Yongxin and Zhang, Li and Xiang, Tao and Torr, Philip H.S. and Hospedales, Timothy M.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00131},
url = {https://mlanthology.org/cvpr/2018/sung2018cvpr-learning/}
}