Methods › General › Regularization › Early Stopping

Early Stopping

introduced 1995 468 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Early Stopping is a regularization technique for deep neural networks that stops training when parameter updates no longer begin to yield improves on a validation set. In essence, we store and update the current best parameters during training, and when parameter updates no longer yield an improvement (after a set number of iterations) we stop training and use the last best parameters. It works as a regularizer by restricting the optimization procedure to a smaller volume of parameter space.

Image Source: Ramazan Gençay

Papers archive 2025-07-28

30 shown of 468, 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

20 shown of 295 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
Image Generation43
regression21
Transfer Learning18
Decision Making17
Conditional Image Generation16
Hyperparameter Optimization16
Image Classification16
Data Augmentation15
Denoising14
Bayesian Optimization13
Deep Learning13
image-classification13
reinforcement-learning13
Model Selection12
General Classification11
Reinforcement Learning11
Benchmarking10
GPU10
Generative Adversarial Network10
Memorization10

Usage over time archive 2025-07-28

Papers per year tagged with Early Stopping: 2013 to 2025, peak 87 87 0 2013: 2 papers 2013 2014: 2 papers 2014 2015: 5 papers 2015 2016: 0 papers 2016 2017: 11 papers 2017 2018: 15 papers 2018 2019: 27 papers 2019 2020: 69 papers 2020 2021: 67 papers 2021 2022: 65 papers 2022 2023: 87 papers 2023 2024: 77 papers 2024 2025: 41 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (468 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

Regularization

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