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Model Soups

Soups

19 papers tagged archive 2025-07-28

Introduced by Mitchell Wortsman et al. in Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

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

Compress an ensemble of models into a single one by averaging their weights (under certain pre-conditions).

PaperSource

Papers archive 2025-07-28

19 shown of 19, 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 22 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
Domain Generalization2
Ensemble Learning1
Federated Learning1
Few-Shot Learning1
GPU1
Graph Sampling1
Image Classification1
Language Modeling1
Language Modelling1
Masked Language Modeling1
Medical Image Analysis1
Model Compression1
Multi-Object Tracking1
Object Tracking1
Out-of-Distribution Generalization1
Transfer Learning1
Translation1
Unsupervised Domain Adaptation1
graph partitioning1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with Soups: 2022 to 2025, peak 8 8 0 2022: 3 papers 2022 2023: 8 papers 2023 2024: 7 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (19 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

Model Compression

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