# SinSim: Sinkhorn-Regularized SimCLR

arXiv: https://arxiv.org/abs/2502.10478 · HTML: https://mlsanity.com/paper/2502.10478/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space](https://mlsanity.com/paper/2509.07289/) (2025)
- [Self-Supervised Learning by Curvature Alignment](https://mlsanity.com/paper/2511.17426/) (2025)

## Abstract (arXiv)

Self-supervised learning has revolutionized representation learning by eliminating the need for labeled data. Contrastive learning methods, such as SimCLR, maximize the agreement between augmented views of an image but lack explicit regularization to enforce a globally structured latent space. This limitation often leads to suboptimal generalization. We propose SinSim, a novel extension of SimCLR that integrates Sinkhorn regularization from optimal transport theory to enhance representation structure. The Sinkhorn loss, an entropy-regularized Wasserstein distance, encourages a well-dispersed and geometry-aware feature space, preserving discriminative power. Empirical evaluations on various datasets demonstrate that SinSim outperforms SimCLR and achieves competitive performance against prominent self-supervised methods such as VICReg and Barlow Twins. UMAP visualizations further reveal improved class separability and structured feature distributions. These results indicate that integrating optimal transport regularization into contrastive learning provides a principled and effective mechanism for learning robust, well-structured representations. Our findings open new directions for applying transport-based constraints in self-supervised learning frameworks.