Our new framework, “Mending Neighbours,” improves contrastive learning by effectively identifying and replacing unhelpful ’neighbour’ representations with our unique “Bridge Points”. This unsupervised method enhances model performance by ensuring only the most informative representations are learned. Outperforming leading methods on three benchmark datasets, our work underscores the crucial role of careful neighbour selection in contrastive learning.