  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).
