  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 networks. A crucial component of MCL is Interactive Contrastive
Learning (ICL). Compared with vanilla contrastive learning, ICL can aggregate
cross-network embedding information and maximize the lower bound to the mutual
information between two networks. This enables each network to learn extra
contrastive knowledge from others, leading to better feature representations
for visual recognition tasks. We emphasize that the resulting 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 image classification and transfer learning to object
detection show that MCL can lead to consistent performance gains, demonstrating
that MCL can guide the network to generate better feature representations. Code
is available at https://github.com/winycg/MCL.

Computer Vision and Pattern Recognition