  Few-shot classification requires adapting knowledge learned from a large
annotated base dataset to recognize novel unseen classes, each represented by
few labeled examples. In such a scenario, pretraining a network with high
capacity on the large dataset and then finetuning it on the few examples causes
severe overfitting. At the same time, training a simple linear classifier on
top of "frozen" features learned from the large labeled dataset fails to adapt
the model to the properties of the novel classes, effectively inducing
underfitting. In this paper we propose an alternative approach to both of these
two popular strategies. First, our method pseudo-labels the entire large
dataset using the linear classifier trained on the novel classes. This
effectively "hallucinates" the novel classes in the large dataset, despite the
novel categories not being present in the base database (novel and base classes
are disjoint). Then, it finetunes the entire model with a distillation loss on
the pseudo-labeled base examples, in addition to the standard cross-entropy
loss on the novel dataset. This step effectively trains the network to
recognize contextual and appearance cues that are useful for the novel-category
recognition but using the entire large-scale base dataset and thus overcoming
the inherent data-scarcity problem of few-shot learning. Despite the simplicity
of the approach, we show that that our method outperforms the state-of-the-art
on four well-established few-shot classification benchmarks.
