Paper

• generalize contrastive learning used in self-supervised learning to the fully supervised setting
• Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes.
• Achieves top-1 accuracy of 81/4% on ResNet-200
• self-supervised contrastive loss contrasts a single positive against a set of negatives