Methods › General › Self-Supervised Learning › COLA

COLA

32 papers tagged archive 2025-07-28

Introduced by Aaqib Saeed et al. in Contrastive Learning of General-Purpose Audio Representations

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

COLA is a self-supervised pre-training approach for learning a general-purpose representation of audio. It is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from different recordings.

PaperSource

Papers archive 2025-07-28

30 shown of 32, 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 63 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
CoLA32
Contrastive Learning5
Language Modelling4
parameter-efficient fine-tuning4
Language Modeling3
Large Language Model3
Linguistic Acceptability3
Text Generation3
Autonomous Driving2
Domain Generalization2
Representation Learning2
Self-Supervised Learning2
Semantic Segmentation2
Sentence2
Topological Data Analysis2
Transfer Learning2
3D Semantic Segmentation1
Action Localization1
Action Recognition1
Anomaly Detection1

Usage over time archive 2025-07-28

Papers per year tagged with COLA: 2020 to 2025, peak 10 10 0 2020: 1 paper 2020 2021: 2 papers 2021 2022: 6 papers 2022 2023: 10 papers 2023 2024: 9 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (32 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

Self-Supervised LearningGenerative Audio Models

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