  In this paper, we propose an approach to improve few-shot classification
performance using a composite rotation based auxiliary task. Few-shot
classification methods aim to produce neural networks that perform well for
classes with a large number of training samples and classes with less number of
training samples. They employ techniques to enable the network to produce
highly discriminative features that are also very generic. Generally, the
better the quality and generic-nature of the features produced by the network,
the better is the performance of the network on few-shot learning. Our approach
aims to train networks to produce such features by using a self-supervised
auxiliary task. Our proposed composite rotation based auxiliary task performs
rotation at two levels, i.e., rotation of patches inside the image (inner
rotation) and rotation of the whole image (outer rotation) and assigns one out
of 16 rotation classes to the modified image. We then simultaneously train for
the composite rotation prediction task along with the original classification
task, which forces the network to learn high-quality generic features that help
improve the few-shot classification performance. We experimentally show that
our approach performs better than existing few-shot learning methods on
multiple benchmark datasets.
