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Refining Self-Supervised Learning in Imaging: Beyond Linear Metric
[article]
2022
arXiv
pre-print
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
arXiv:2202.12921v2
fatcat:gojx5muezjdybeyjd66exmwfly