Methods › General › Robust Training › Self-adaptive Training
Self-adaptive Training
Introduced by Lang Huang et al. in Self-Adaptive Training: beyond Empirical Risk Minimization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Self-adaptive Training is a training algorithm that dynamically corrects problematic training labels by model predictions to improve generalization of deep learning for potentially corrupted training data. Accumulated predictions are used to augment the training dynamics. The use of an exponential-moving-average scheme alleviates the instability issue of model predictions, smooths out the training target during the training process and enables the algorithm to completely change the training labels if necessary.
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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Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms 29 Oct 2022 · 2 repositories · arXiv:2210.16575
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SAT: Self-adaptive training for fashion compatibility prediction 25 Jun 2022 · 1 repository · arXiv:2206.12622
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Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning 21 Jan 2021 · 2 repositories · arXiv:2101.08732
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Self-Adaptive Training: beyond Empirical Risk Minimization 24 Feb 2020 · 4 repositories · arXiv:2002.10319Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
12 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
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