There is a bunch of Contrastive Representation Learning methods exist already, e.g. MoCo, SimCLR, BYOL, etc.
Here is another one - CLIM: Center-wise local image muxture for contrastive representation learning (ICLR 2021).

The main idea is to consider the semantic similarity between different images and incorporate it in the learning procedure, in contrast to the many contrastive learning methods which usually use augmentations of the query image as positives. The main contribution is 2-fold:
a) partition data in 10k clusters, use nearest neighbors from the same cluster which are closer to the centroid than the anchor as positive samples;
b) use more complex augmentations, i.e. CutMix and multi-resolution during training. The proposed method achieves state-of-the-art results for unsupervised learning on Imagenet and transfer learning tasks Pascal VOC, COCO, and LVIS.