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.

翻译：支持-查询偏移小样本学习旨在基于低维空间中学到的嵌入，在支持集与查询集之间存在分布偏移的情况下，将未见示例（查询集）分类到已标记数据（支持集）。然而，在实际场景中，这类偏移通常未知且多变，导致难以预先估计。因此，本文提出一个新颖但更具挑战性的难题——RSQS，关注真实支持-查询偏移小样本学习。RSQS的关键特征在于，元任务中的单个样本在每次元任务中会经历多重分布偏移。此外，我们提出一种统一的对抗特征对齐方法，称为双对抗对齐框架（DuaL），从域间偏差和域内方差两个层面缓解RSQS。一方面，针对域间偏差，我们预先破坏原始数据，利用合成的扰动输入训练修复网络，通过在特征层面最小化距离来实现。另一方面，针对域内方差，我们提出一个生成器网络，以自监督方式从支持集中合成困难（即相似度较低）示例，并引入正则化最优传输以推导平滑的最优传输方案。最后，我们在三个数据集（CIFAR100、mini-ImageNet和Tiered-Imagenet）上基于多个最先进基准方法构建了RSQS的基准测试。实验结果表明，DuaL在我们的基准测试中显著优于现有最先进方法。