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

  title={Leveraging the Feature Distribution in Transfer-based Few-Shot Learning},
  author={Yuqing Hu and Vincent Gripon and St{\'e}phane Pateux},
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. In the past few years, transfer-based methods have proved to achieve the best performance, thanks to well-thought-out backbone architectures combined with efficient postprocessing steps. Following this vein, in this paper we propose a transfer-based novel method that builds on two steps: 1) preprocessing the feature vectors so that they become closer to Gaussian-like distributions, and… 

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  • Hao ZhuPiotr Koniusz
  • Computer Science
    2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2022
An unsupErvised discriminAnt Subspace lEarning (EASE) that improves transductive few-shot learning performance by learning a linear projection onto a subspace built from features of the support set and the unlabeled query set in the test time is presented.

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