On the Properties of Feature Attribution for Supervised Contrastive Learning
Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit classification layer. However, there exist alternative approaches, such as Contrastive Learning (CL). Instead of explicitly operating a classification, CL has the NN produce an embedding space where projections of similar data are pulled together, while projections of dissimilar data are pushed apart. In the case of Supervised CL (SCL), labels are adopted as similarity criteria, thus creating an embedding space where the projected data points are well-clustered. SCL provides crucial advantages over CE with regard to adversarial robustness a...
| Authors | Leonardo Arrighi, Julia Eva Belloni, Aurélie Gallet et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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