Methods › General › Self-Supervised Learning › RotNet
RotNet
Introduced by J. Emmanuel Johnson et al. in RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
RotNet is a self-supervision approach that relies on predicting image rotations as the pretext task in order to learn image representations.
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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ScaleNet: An Unsupervised Representation Learning Method for Limited Information 3 Oct 2023 · 0 repositories · arXiv:2310.02386
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Test-time Training for Data-efficient UCDR 19 Aug 2022 · 1 repository · arXiv:2208.09198
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Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition 28 May 2021 · 0 repositories · arXiv:2105.13557
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RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks 2 Dec 2020 · 0 repositories · arXiv:2012.01985
Tasks archive 2025-07-28
13 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