This repository provides the official PyTorch implementation of a CVPR 2020 paper on few-shot learning. The method adapts pre-trained instance embeddings to target classification tasks using set-to-set functions, with the Transformer architecture proving most effective. The approach is validated on standard few-shot learning benchmarks including MiniImageNet using ResNet-12 backbones, demonstrating improved discriminative task-specific embeddings.