Mutual Contrastive Learning for Visual Representation Learning

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 contrastive distributions among cohort networks. A crucial component Interactive (ICL). Compared with vanilla learning, ICL can aggregate cross-network embedding information maximize the lower bound between two This enables each network learn extra knowledge from others, leading better feature representations recognition tasks. emphasize that resulting conceptually simple yet empirically powerful. It generic framework be applied both supervised self-supervised Experimental results on image classification object detection show lead consistent performance gains, demonstrating guide generate representations. Code available at https://github.com/winycg/MCL.