Methods › General › Robust Training › Self-adaptive Training

Self-adaptive Training

4 papers tagged archive 2025-07-28

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.

PaperSource

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.

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.

TaskPapers
Autonomous Driving1
Diversity1
General Classification1
Linear evaluation1
Prediction1
Reinforcement Learning (RL)1
Representation Learning1
Safe Reinforcement Learning1
Self-Learning1
Self-Supervised Learning1
Transfer Learning1
Triplet1

Usage over time archive 2025-07-28

Papers per year tagged with Self-adaptive Training: 2020 to 2022, peak 2 2 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 2 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

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

Robust Training

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