Selecting Relevant Features from a Multi-Domain Representation for Few-Shot Classification
Abstract
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.
Cite
Text
Dvornik et al. "Selecting Relevant Features from a Multi-Domain Representation for Few-Shot Classification." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58607-2_45Markdown
[Dvornik et al. "Selecting Relevant Features from a Multi-Domain Representation for Few-Shot Classification." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/dvornik2020eccv-selecting/) doi:10.1007/978-3-030-58607-2_45BibTeX
@inproceedings{dvornik2020eccv-selecting,
title = {{Selecting Relevant Features from a Multi-Domain Representation for Few-Shot Classification}},
author = {Dvornik, Nikita and Schmid, Cordelia and Mairal, Julien},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58607-2_45},
url = {https://mlanthology.org/eccv/2020/dvornik2020eccv-selecting/}
}