  In the past few years, contrastive learning has played a central role for the
success of visual unsupervised representation learning. Around the same time,
high-performance non-contrastive learning methods have been developed as well.
While most of the works utilize only two views, we carefully review the
existing multi-view methods and propose a general multi-view strategy that can
improve learning speed and performance of any contrastive or non-contrastive
method. We first analyze CMC's full-graph paradigm and empirically show that
the learning speed of $K$-views can be increased by $_{K}\mathrm{C}_{2}$ times
for small learning rate and early training. Then, we upgrade CMC's full-graph
by mixing views created by a crop-only augmentation, adopting small-size views
as in SwAV multi-crop, and modifying the negative sampling. The resulting
multi-view strategy is called ECPP (Efficient Combinatorial Positive Pairing).
We investigate the effectiveness of ECPP by applying it to SimCLR and assessing
the linear evaluation performance for CIFAR-10 and ImageNet-100. For each
benchmark, we achieve a state-of-the-art performance. In case of ImageNet-100,
ECPP boosted SimCLR outperforms supervised learning.
