# Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions

@article{Johnson2022ContrastiveLC, title={Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions}, author={Daniel D. Johnson and Ayoub El Hanchi and Chris J. Maddison}, journal={ArXiv}, year={2022}, volume={abs/2210.01883} }

Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contrastive learning methods can be reinterpreted as learning kernel functions that approximate a fixed positive-pair kernel. We then prove that a simple representation obtained by combining this kernel with PCA provably minimizes the worst-case approximation error of linear predictors, under a straightforward assumption…

## One Citation

### Neural Eigenfunctions Are Structured Representation Learners

- Computer ScienceArXiv
- 2022

This paper shows that, when the kernel is derived from positive relations in a contrastive learning setup, the method outperforms a number of competitive baselines in visual representation learning and transfer learning benchmarks, and importantly, produces structured representations where the order of features indicates degrees of importance.

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