  Instance discrimination based contrastive learning has emerged as a leading
approach for self-supervised learning of visual representations. Yet, its
generalization to novel tasks remains elusive when compared to representations
learned with supervision, especially in the few-shot setting. We demonstrate
how one can incorporate supervision in the instance discrimination based
contrastive self-supervised learning framework to learn representations that
generalize better to novel tasks. We call our approach CIDS (Contrastive
Instance Discrimination with Supervision). CIDS performs favorably compared to
existing algorithms on popular few-shot benchmarks like Mini-ImageNet or
Tiered-ImageNet. We also propose a novel model selection algorithm that can be
used in conjunction with a universal embedding trained using CIDS to outperform
state-of-the-art algorithms on the challenging Meta-Dataset benchmark.
