  The popularity of self-supervised learning has made it possible to train
models without relying on labeled data, which saves expensive annotation costs.
However, most existing self-supervised contrastive learning methods often
overlook the combination of global and local feature information. This paper
proposes a multi-network contrastive learning framework based on global and
local representations. We introduce global and local feature information for
self-supervised contrastive learning through multiple networks. The model
learns feature information at different scales of an image by contrasting the
embedding pairs generated by multiple networks. The framework also expands the
number of samples used for contrast and improves the training efficiency of the
model. Linear evaluation results on three benchmark datasets show that our
method outperforms several existing classical self-supervised learning methods.
