Fine-tuning a deep network trained with the standard cross-entropy loss is a
strong baseline for few-shot learning. When fine-tuned transductively, this
outperforms the current state-of-the-art on standard datasets such as
Mini-ImageNet, Tiered-ImageNet, CIFAR-FS and FC-100 with the same
hyper-parameters. The simplicity of this approach enables us to demonstrate the
first few-shot learning results on the ImageNet-21k dataset. We find that using
a large number of meta-training classes results in high few-shot accuracies
even for a large number of few-shot classes. We do not advocate our approach as
the solution for few-shot learning, but simply use the results to highlight
limitations of current benchmarks and few-shot protocols. We perform extensive
studies on benchmark datasets to propose a metric that quantifies the
"hardness" of a few-shot episode. This metric can be used to report the
performance of few-shot algorithms in a more systematic way.

这篇研究论文旨在通过优化跨熵损失训练深度网络，提出一种基准模型来解决 few-shot 学习问题，并通过使用更多的元训练类来提高准确性，同时提出衡量 few-shot 任 务难度的度量标准。