  The high cost of acquiring and annotating samples has made the `few-shot'
learning problem of prime importance. Existing works mainly focus on improving
performance on clean data and overlook robustness concerns on the data
perturbed with adversarial noise. Recently, a few efforts have been made to
combine the few-shot problem with the robustness objective using sophisticated
Meta-Learning techniques. These methods rely on the generation of adversarial
samples in every episode of training, which further adds a computational
burden. To avoid such time-consuming and complicated procedures, we propose a
simple but effective alternative that does not require any adversarial samples.
Inspired by the cognitive decision-making process in humans, we enforce
high-level feature matching between the base class data and their corresponding
low-frequency samples in the pretraining stage via self distillation. The model
is then fine-tuned on the samples of novel classes where we additionally
improve the discriminability of low-frequency query set features via cosine
similarity. On a 1-shot setting of the CIFAR-FS dataset, our method yields a
massive improvement of $60.55\%$ & $62.05\%$ in adversarial accuracy on the PGD
and state-of-the-art Auto Attack, respectively, with a minor drop in clean
accuracy compared to the baseline. Moreover, our method only takes $1.69\times$
of the standard training time while being $\approx$ $5\times$ faster than
state-of-the-art adversarial meta-learning methods. The code is available at
https://github.com/vcl-iisc/robust-few-shot-learning.
