Methods › General › Pruning › Dataset Pruning

Dataset Pruning

25 papers tagged archive 2025-07-28

Introduced by Shuo Yang et al. in Dataset Pruning: Reducing Training Data by Examining Generalization Influence

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

Dataset pruning is an approach to reduce a large dataset to obtain a small dataset by removing less significant sample.

PaperSource

Papers archive 2025-07-28

25 shown of 25, 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 32 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
Diversity2
Federated Learning2
Image Classification2
Natural Language Understanding2
image-classification2
8k1
All1
Classification1
Computational Efficiency1
Dataset Condensation1
Dataset Distillation1
Density Estimation1
Fairness1
GPU1
Graph Embedding1
Image Captioning1
Image Classification with Label Noise1
Image Super-Resolution1
Inductive Bias1
Informativeness1

Usage over time archive 2025-07-28

Papers per year tagged with Dataset Pruning: 2022 to 2025, peak 13 13 0 2022: 2 papers 2022 2023: 6 papers 2023 2024: 13 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (25 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

Pruning

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