Few‐shot classification using Gaussianisation prototypical classifier

Abstract

Abstract Few‐shot classification (FSC) aims at classifying query samples into correct classes given only a few labelled samples. Prototypical Classifier (PC) can be chosen to be an ideal classifier for settling this problem, as it has good properties of low‐capacity and parameter‐free. However, the mean‐based prototypes suffer from the issue of deviating from its ground‐truth centre. In order to solve such problem of prototype bias, Gaussianisation Prototypical Classifier (GPC) is proposed, which is a kind of one‐step prototype rectification method. Specifically, the authors first perform Gaussianisation operation over the feature extracted from the backbone network so that the features fit the particular Gaussian distribution. Second, the authors use prototype feature of the base class as prior information and employs Maximum a Posteriori estimation method to obtain the reliable prototype for each novel class. Finally, the query sample of novel class is classified to be its nearest prototype with non‐parametric classifiers. Extensive experiments have been conducted on multiple FSC benchmarks. Comparative results also demonstrate that the authors’ method is superior to existing state‐of‐the‐art FSC methods.

Keywords

• few‐shot classification
• maximum a posteriori
• reliable prototype