Methods › General › Meta-Learning Algorithms › Meta-augmentation

Meta-augmentation

4 papers tagged archive 2025-07-28

Introduced by Janarthanan Rajendran et al. in Meta-Learning Requires Meta-Augmentation

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

Meta-augmentation helps generate more varied tasks for a single example in meta-learning. It can be distinguished from data augmentation in classic machine learning as follows. For data augmentation in classical machine learning, the aim is to generate more varied examples, within a single task. Meta-augmentation has the exact opposite aim: we wish to generate more varied tasks, for a single example, to force the learner to quickly learn a new task from feedback. In meta-augmentation, adding randomness discourages the base learner and model from learning trivial solutions that do not generalize to new tasks.

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

7 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
Meta-Learning3
Contrastive Learning1
Data Augmentation1
Domain Adaptation1
Sequential Recommendation1
Transfer Learning1
regression1

Usage over time archive 2025-07-28

Papers per year tagged with Meta-augmentation: 2020 to 2023, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 2 papers 2022 2023: 1 paper 2023
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

Meta-Learning Algorithms

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