Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning approaches with adversarial training to improve the robustness of few-shot classifiers. We show that a simple transfer-learning based approach can be used to train adversarially robust few-shot classifiers. We also present a method for novel classification task based on calibrating the centroid of the few-shot category towards the base classes. We show that standard adversarial training on base categories along with calibrated centroid-based classifier in the novel categories, outperforms or is on-par with state-of-the-art advanced methods on standard benchmarks for few-shot learning. Our method is simple, easy to scale, and with little effort can lead to robust few-shot classifiers. Code is available here: \url{https://github.com/UCDvision/Simple_few_shot.git}

本文研究了如何使用转移学习训练鲁棒的少样本分类器，并提出了一种基于校准中心点的分类方法。我们证明，将标准对抗训练应用于基础类别，并结合针对新类别的校准中心点分类器，可以在少样本学习的标准基准测试中超越或与最先进的高级方法持平。我们的方法简单易用，易于扩展，几乎不需要额外的工作量就能使少样本分类器更加稳健。

Few-shot图像分类中的敌对鲁棒性简单方法