NNCLR learns self-supervised representations that go beyond single-instance positives, which allows for learning better features that are invariant to different viewpoints, deformations, and even intra-class variations. Clustering based methods offer a great approach to go beyond single instance positives, but assuming the entire cluster to be positives could hurt performance due to early over-generalization. Instead, NNCLR uses nearest neighbors in the learned representation space as positives. In addition, NNCLR increases the performance of existing contrastive learning methods like SimCLR(Keras Example) and reduces the reliance of self-supervised methods on data augmentation strategies.