Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification

Understand

In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples.

• In this context, works have proposed to introduce Graph Neural Networks (GNNs) aiming at exploiting the information contained in other samples treated concurrently, what is commonly referred to as the transductive setting in the literature.
• These GNNs are trained all together with a backbone feature extractor.
• In this paper, we propose a new method that relies on graphs only to interpolate feature vectors instead, resulting in a transductive learning setting with no additional parameters to train.