The quality and generality of deep image features is crucially determined by
the data they have been trained on, but little is known about this often
overlooked effect. In this paper, we systematically study the effect of
variations in the training data by evaluating deep features trained on
different image sets in a few-shot classification setting. The experimental
protocol we define allows to explore key practical questions. What is the
influence of the similarity between base and test classes? Given a fixed
annotation budget, what is the optimal trade-off between the number of images
per class and the number of classes? Given a fixed dataset, can features be
improved by splitting or combining different classes? Should simple or diverse
classes be annotated? In a wide range of experiments, we provide clear answers
to these questions on the miniImageNet, ImageNet and CUB-200 benchmarks. We
also show how the base dataset design can improve performance in few-shot
classification more drastically than replacing a simple baseline by an advanced
state of the art algorithm.

本文系统地研究了训练数据变化对深度图像特征的质量和通用性的影响，并提出了关键实用问题，如在固定注释预算情况下，每类图像数量和类别数量之间的最佳权衡