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An Analysis of Self-supervised Pre-training with Dependent Samples

This story is from 2026-09-07. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.05031v1 Announce Type: new Abstract: Self-supervised learning relies on so-called data augmentations $\phi(x)$ of unlabeled datapoints $x$ --- for example, masking random pixels in an image $x$ --- that should leave the label of $x$ invariant and are often used to learn a lower-complexit…

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  1. 2026-09-07 04:00 · arXiv stat.ML
    An Analysis of Self-supervised Pre-training with Dependent Samples

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