Which Tasks Survive Self-Supervised Learning?
Quick summary
arXiv:2609.38393v1 Announce Type: cross Abstract: Same-instance self-supervised learning (SSL) learns representations by enforcing consistency across two views of the same underlying instance. This principle alone, however, does not determine which downstream tasks remain recoverable from the learned representation. We study this question through \emph{semantic recoverability}, defined as the amount of a task's posterior score captured by the represented function space. We show that, for centered and whitened representations, recoverability exactly determines directional class-distance-normali
Key takeaways
- arXiv:2609.38393v1 Announce Type: cross Abstract: Same-instance self-supervised learning (SSL) learns representations by enforcing consistency across two views of the same underlying instance.
- This principle alone, however, does not determine which downstream tasks remain recoverable from the learned representation.
- We study this question through \emph{semantic recoverability}, defined as the amount of a task's posterior score captured by the represented function space.
Why it matters
“Which Tasks Survive Self-Supervised Learning?” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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