  We present a collaborative learning method called Mutual Contrastive Learning
(MCL) for general visual representation learning. The core idea of MCL is to
perform mutual interaction and transfer of contrastive distributions among a
cohort of models. Benefiting from MCL, each model can learn extra contrastive
knowledge from others, leading to more meaningful feature representations for
visual recognition tasks. We emphasize that MCL is conceptually simple yet
empirically powerful. It is a generic framework that can be applied to both
supervised and self-supervised representation learning. Experimental results on
supervised and self-supervised image classification, transfer learning and
few-shot learning show that MCL can lead to consistent performance gains,
demonstrating that MCL can guide the network to generate better feature
representations.
