  Learning from a few examples is an important practical aspect of training
classifiers. Various works have examined this aspect quite well. However, all
existing approaches assume that the few examples provided are always correctly
labeled. This is a strong assumption, especially if one considers the current
techniques for labeling using crowd-based labeling services. We address this
issue by proposing a novel robust few-shot learning approach. Our method relies
on generating robust prototypes from a set of few examples. Specifically, our
method refines the class prototypes by producing hybrid features from the
support examples of each class. The refined prototypes help to classify the
query images better. Our method can replace the evaluation phase of any
few-shot learning method that uses a nearest neighbor prototype-based
evaluation procedure to make them robust. We evaluate our method on standard
mini-ImageNet and tiered-ImageNet datasets. We perform experiments with various
label corruption rates in the support examples of the few-shot classes. We
obtain significant improvement over widely used few-shot learning methods that
suffer significant performance degeneration in the presence of label noise. We
finally provide extensive ablation experiments to validate our method.
