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。

Few-Shot Learning中的Shot数量理论分析