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

By Yuqing Hu, Vincent Gripon and Stéphane 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 2) leveraging this preprocessing using an optimal-transport inspired algorithm. 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

Topics: Computer Science - Machine Learning, Statistics - Machine Learning
Year: 2020
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