  The pursuit of learning robust representations without human supervision is a
longstanding challenge. The recent advancements in self-supervised contrastive
learning approaches have demonstrated high performance across various
representation learning challenges. However, current methods depend on the
random transformation of training examples, resulting in some cases of
unrepresentative positive pairs that can have a large impact on learning. This
limitation not only impedes the convergence of the learning process but the
robustness of the learnt representation as well as requiring larger batch sizes
to improve robustness to such bad batches. This paper attempts to alleviate the
influence of false positive and false negative pairs by employing pairwise
similarity calculations through the Fr\'echet ResNet Distance (FRD), thereby
obtaining robust representations from unlabelled data. The effectiveness of the
proposed method is substantiated by empirical results, where a linear
classifier trained on self-supervised contrastive representations achieved an
impressive 87.74\% top-1 accuracy on STL10 and 99.31\% on the Flower102
dataset. These results emphasize the potential of the proposed approach in
pushing the boundaries of the state-of-the-art in self-supervised contrastive
learning, particularly for image classification tasks.
