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Leveraging the Feature Distribution in Transfer-based Few-Shot Learning

Y. H. V. Gripon et S. Pateux, "Leveraging the Feature Distribution in Transfer-based Few-Shot Learning," dans ArXiv Preprint, 2020.

Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. In the past few years, many methods have been proposed to solve few-shot classification, among which transfer-based methods have proved to achieve the best performance. Following this vein, in this paper we propose a novel transfer-based method that builds on two successive steps: 1) preprocessing the feature vectors so that they become closer to Gaussian-like distributions, and 2) leveraging this preprocessing using an optimal-transport inspired algorithm (in the case of transductive settings). Using standardized vision benchmarks, we prove the ability of the proposed methodology to achieve state-of-the-art accuracy with various datasets, backbone architectures and few-shot settings.

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Bibtex
@inproceedings{GriPat2020,
  author = {Yuqing Hu, Vincent Gripon and Stéphane
Pateux},
  title = {Leveraging the Feature Distribution in
Transfer-based Few-Shot Learning},
  booktitle = {ArXiv Preprint},
  year = {2020},
}




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