  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.
