  Few-shot learning aims to transfer information from one task to enable
generalization on novel tasks given a few examples. This information is present
both in the domain and the class labels. In this work we investigate the
complementary roles of these two sources of information by combining
instance-discriminative contrastive learning and supervised learning in a
single framework called Supervised Momentum Contrastive learning (SUPMOCO). Our
approach avoids a problem observed in supervised learning where information in
images not relevant to the task is discarded, which hampers their
generalization to novel tasks. We show that (self-supervised) contrastive
learning and supervised learning are mutually beneficial, leading to a new
state-of-the-art on the META-DATASET - a recently introduced benchmark for
few-shot learning. Our method is based on a simple modification of MOCO and
scales better than prior work on combining supervised and self-supervised
learning. This allows us to easily combine data from multiple domains leading
to further improvements.
