Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier
Abstract
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification. We show experimentally that a library of pre-trained feature extractors combined with a simple feed-forward network learned with an L2-regularizer can be an excellent option for solving cross-domain few-shot image classification. Our experimental results suggest that this simpler sample-efficient approach far outperforms several well-established meta-learning algorithms.
Cite
Text
Chowdhury et al. "Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.00931Markdown
[Chowdhury et al. "Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/chowdhury2021iccv-fewshot/) doi:10.1109/ICCV48922.2021.00931BibTeX
@inproceedings{chowdhury2021iccv-fewshot,
title = {{Few-Shot Image Classification: Just Use a Library of Pre-Trained Feature Extractors and a Simple Classifier}},
author = {Chowdhury, Arkabandhu and Jiang, Mingchao and Chaudhuri, Swarat and Jermaine, Chris},
booktitle = {International Conference on Computer Vision},
year = {2021},
pages = {9445-9454},
doi = {10.1109/ICCV48922.2021.00931},
url = {https://mlanthology.org/iccv/2021/chowdhury2021iccv-fewshot/}
}