Few-shot classification is the task of predicting the category of an example
from a set of few labeled examples. The number of labeled examples per category
is called the number of shots (or shot number). Recent works tackle this task
through meta-learning, where a meta-learner extracts information from observed
tasks during meta-training to quickly adapt to new tasks during meta-testing.
In this formulation, the number of shots exploited during meta-training has an
impact on the recognition performance at meta-test time. Generally, the shot
number used in meta-training should match the one used in meta-testing to
obtain the best performance. We introduce a theoretical analysis of the impact
of the shot number on Prototypical Networks, a state-of-the-art few-shot
classification method. From our analysis, we propose a simple method that is
robust to the choice of shot number used during meta-training, which is a
crucial hyperparameter. The performance of our model trained for an arbitrary
meta-training shot number shows great performance for different values of
meta-testing shot numbers. We experimentally demonstrate our approach on
different few-shot classification benchmarks.

本文研究了现有少样本分类方法，提出了一种鲁棒性较好的方法来应对培训数据量和测试数据量不匹配等情况。该方法通过理论分析，将嵌套式学习（meta-learning）与原型网络（Prototypical Networks）相结合，以研究最佳 “Shot Number”（样本数量数），并取得了在少量训练数据和多量测试数据情况下良好的性能表现。