Improving Image Classifiers with Supervised Contrastive Learning


In this report, we will discuss a new training methodology, namely supervised contrastive learning(SCL) introduced in this paper by Khosla et al. This methodology is an adaption of contrastive learning in the field of fully supervised learning problems. The method uses label information to cluster samples belonging to the same class in the embedding space. On top of it, a linear classifier can be used to classify the images. This method is said to be outperforming cross-entropy.

Here in this article, we will discuss the proposed methodology in detail, followed by running three experiments to check how it performs compared to Cross-Entropy.

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