Few shot classification aims to learn to recognize novel categories
using only limited samples per category. Most current few shot methods
use a base dataset rich in labeled examples to train an encoder that
is used for obtaining representations of support instances for novel classes. Since
the test instances are from a distribution different to the base
distribution, their feature representations are of poor quality,
degrading performance. In this paper we propose to make
use of the well-trained feature representations of the base dataset that
are closest to each support instance to improve its representation
during meta-test time. To this end, we propose BaseTransformers, that attends to the most relevant regions of the base dataset
feature space and improves support instance representations. Experiments on three benchmark data
sets show that our method works well for several backbones and
achieves state-of-the-art results in the inductive one shot setting. Code is available at github.com/mayug/BaseTransformers .