论文标题
基本数据集设计对几张图像分类的影响
Impact of base dataset design on few-shot image classification
论文作者
论文摘要
深层图像特征的质量和普遍性取决于他们已经训练的数据，但对这种经常被忽略的效果知之甚少。在本文中，我们通过评估在几个射击分类设置中对不同图像集训练的深度特征，系统地研究训练数据中的变化效果。我们定义的实验协议允许探索关键的实际问题。基础和测试类之间的相似性有什么影响？鉴于固定的注释预算，每个班级的图像数量与班级数量之间的最佳权衡是什么？鉴于固定的数据集，可以通过分裂或组合不同的类来改善功能吗？应该注释简单或多样化的课程吗？在广泛的实验中，我们为这些问题提供了有关迷你Imimagenet，ImageNet和Cub-200基准测试的明确答案。我们还展示了基本数据集设计如何比通过先进的最先进的算法更换简单的基线来更大程度地提高几次射击分类的性能。
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.
