In this report, we relate the algorithmic design of Barlow Twins' method to the Hilbert-Schmidt Independence Criterion (HSIC), thus establishing it as a contrastive learning approach that is free of negative samples. Through this perspective, we argue that Barlow Twins (and thus the class of negative-sample-free contrastive learning methods) suggests a possibility to bridge the two major families of self-supervised learning philosophies: non-contrastive and contrastive approaches. In particular, Barlow twins exemplified how we could combine the best practices of both worlds: avoiding the need of large training batch size and negative sample pairing (like non-contrastive methods) and avoiding symmetry-breaking network designs (like contrastive methods).

本研究将Barlow Twins方法的算法设计与Hilbert-Schmidt独立准则（HSIC）联系起来，并将其作为一种不需要负样本的对比学习方法，通过这个视角，我们认为Barlow Twins提出了一种可能性，即将自监督学习思想的两大流派：非对比和对比方法结合起来，这在避免与非对比方法一样需要大型训练批次和负样本配对以及避免对比方法的对称网络设计等方面发挥了最佳实践的优势。

关于连接贝洛双胞胎与无负样本对比学习的注记