Methods › General › Self-Supervised Learning › NPID
NPID
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
NPID (Non-Parametric Instance Discrimination) is a self-supervision approach that takes a non-parametric classification approach. Noise contrastive estimation is used to learn representations. Specifically, distances (similarity) between instances are calculated directly from the features in a non-parametric way.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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How Well Do Self-Supervised Models Transfer? 26 Nov 2020 · 1 repository · arXiv:2011.13377Syntology ran 4 of 5 samples · 1 unverified
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Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination 9 Aug 2020 · 2 repositories · arXiv:2008.03813Syntology ran 2 of 8 samples · 6 unverified
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Self-Supervised Learning of Pretext-Invariant Representations 4 Dec 2019 · 7 repositories · arXiv:1912.01991Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
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Unsupervised Feature Learning via Non-Parametric Instance Discrimination 1 Jun 2018 · 4 repositories
Tasks archive 2025-07-28
17 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections