  We introduce in this paper a new statistical perspective, exploiting the
Jaccard similarity metric, as a measure-based metric to effectively invoke
non-linear features in the loss of self-supervised contrastive learning.
Specifically, our proposed metric may be interpreted as a dependence measure
between two adapted projections learned from the so-called latent
representations. This is in contrast to the cosine similarity measure in the
conventional contrastive learning model, which accounts for correlation
information. To the best of our knowledge, this effectively non-linearly fused
information embedded in the Jaccard similarity, is novel to self-supervision
learning with promising results. The proposed approach is compared to two
state-of-the-art self-supervised contrastive learning methods on three image
datasets. We not only demonstrate its amenable applicability in current ML
problems, but also its improved performance and training efficiency.
