  Support-query shift few-shot learning aims to classify unseen examples (query
set) to labeled data (support set) based on the learned embedding in a
low-dimensional space under a distribution shift between the support set and
the query set. However, in real-world scenarios the shifts are usually unknown
and varied, making it difficult to estimate in advance. Therefore, in this
paper, we propose a novel but more difficult challenge, RSQS, focusing on
Realistic Support-Query Shift few-shot learning. The key feature of RSQS is
that the individual samples in a meta-task are subjected to multiple
distribution shifts in each meta-task. In addition, we propose a unified
adversarial feature alignment method called DUal adversarial ALignment
framework (DuaL) to relieve RSQS from two aspects, i.e., inter-domain bias and
intra-domain variance. On the one hand, for the inter-domain bias, we corrupt
the original data in advance and use the synthesized perturbed inputs to train
the repairer network by minimizing distance in the feature level. On the other
hand, for intra-domain variance, we proposed a generator network to synthesize
hard, i.e., less similar, examples from the support set in a self-supervised
manner and introduce regularized optimal transportation to derive a smooth
optimal transportation plan. Lastly, a benchmark of RSQS is built with several
state-of-the-art baselines among three datasets (CIFAR100, mini-ImageNet, and
Tiered-Imagenet). Experiment results show that DuaL significantly outperforms
the state-of-the-art methods in our benchmark.
