  Contrastive learning and supervised learning have both seen significant
progress and success. However, thus far they have largely been treated as two
separate objectives, brought together only by having a shared neural network.
In this paper we show that through the perspective of hybrid
discriminative-generative training of energy-based models we can make a direct
connection between contrastive learning and supervised learning. Beyond
presenting this unified view, we show our specific choice of approximation of
the energy-based loss outperforms the existing practice in terms of
classification accuracy of WideResNet on CIFAR-10 and CIFAR-100. It also leads
to improved performance on robustness, out-of-distribution detection, and
calibration.
