Few-shot image classification aims at training a model from only a few
examples for each of the "novel" classes. This paper proposes the idea of
associative alignment for leveraging part of the base data by aligning the
novel training instances to the closely related ones in the base training set.
This expands the size of the effective novel training set by adding extra
"related base" instances to the few novel ones, thereby allowing a constructive
fine-tuning. We propose two associative alignment strategies: 1) a
metric-learning loss for minimizing the distance between related base samples
and the centroid of novel instances in the feature space, and 2) a conditional
adversarial alignment loss based on the Wasserstein distance. Experiments on
four standard datasets and three backbones demonstrate that combining our
centroid-based alignment loss results in absolute accuracy improvements of
4.4%, 1.2%, and 6.2% in 5-shot learning over the state of the art for object
recognition, fine-grained classification, and cross-domain adaptation,
respectively.

本论文提出了关联对齐的思想，将新颖的训练实例与基础训练集中密切相关的样本进行对齐，从而扩大了有效新颖训练集的规模。研究表明采用我们提出的关联对齐策略和以质心为基础的关联损失，相较于现有技术，可以分别在目标识别、细粒度分类和跨领域适应中提高 5-shot 学习的绝对准确率 4.4％、1.2％和 6.2％。