Prototypes-oriented Transductive Few-shot Learning with Conditional
  Transport
Long Tian Jingyi Feng Wenchao Chen Xiaoqiang Chai Liming Wang Xiyang Liu Bo Chen
Transductive Few-Shot Learning (TFSL) has recently attracted increasing
attention since it typically outperforms its inductive peer by leveraging
statistics of query samples. However, previous TFSL methods usually encode
uniform prior that all the classes within query samples are equally likely,
which is biased in imbalanced TFSL and causes severe performance degradation.
  Given this pivotal issue, in this work, we propose a novel Conditional
Transport (CT) based imbalanced TFSL model called {\textbf P}rototypes-oriented
{\textbf U}nbiased {\textbf T}ransfer {\textbf M}odel (PUTM) to fully exploit
unbiased statistics of imbalanced query samples, which employs forward and
backward navigators as transport matrices to balance the prior of query samples
per class between uniform and adaptive data-driven distributions. For
efficiently transferring statistics learned by CT, we further derive a closed
form solution to refine prototypes based on MAP given the learned navigators.
The above two steps of discovering and transferring unbiased statistics follow
an iterative manner, formulating our EM-based solver.
  Experimental results on four standard benchmarks including miniImageNet,
tieredImageNet, CUB, and CIFAR-FS demonstrate superiority of our model in
class-imbalanced generalization.