  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.
