As shown in the experiments, using the supervised contrastive learning technique outperformed the conventional technique in terms of the test accuracy. Note that the same training budget (i.e., number of epochs) was given to each technique. Supervised contrastive learning pays off when the encoder involves a complex architecture, like ResNet, and multi-class problems with many labels. In addition, large batch sizes and multi-layer projection heads improve its effectiveness. See the Supervised Contrastive Learning paper for more details.