  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}
