  Few-shot learning (FSL) aims to generate a classifier using limited labeled
examples. Many existing works take the meta-learning approach, constructing a
few-shot learner that can learn from few-shot examples to generate a
classifier. Typically, the few-shot learner is constructed or meta-trained by
sampling multiple few-shot tasks in turn and optimizing the few-shot learner's
performance in generating classifiers for those tasks. The performance is
measured by how well the resulting classifiers classify the test (i.e., query)
examples of those tasks. In this paper, we point out two potential weaknesses
of this approach. First, the sampled query examples may not provide sufficient
supervision for meta-training the few-shot learner. Second, the effectiveness
of meta-learning diminishes sharply with the increasing number of shots. To
resolve these issues, we propose a novel meta-training objective for the
few-shot learner, which is to encourage the few-shot learner to generate
classifiers that perform like strong classifiers. Concretely, we associate each
sampled few-shot task with a strong classifier, which is trained with ample
labeled examples. The strong classifiers can be seen as the target classifiers
that we hope the few-shot learner to generate given few-shot examples, and we
use the strong classifiers to supervise the few-shot learner. We present an
efficient way to construct the strong classifier, making our proposed objective
an easily plug-and-play term to existing meta-learning based FSL methods. We
validate our approach, LastShot, in combinations with many representative
meta-learning methods. On several benchmark datasets, our approach leads to a
notable improvement across a variety of tasks. More importantly, with our
approach, meta-learning based FSL methods can outperform non-meta-learning
based methods at different numbers of shots.
