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Contrastive Multiview Coding

5 papers tagged archive 2025-07-28

Introduced by Yonglong Tian et al. in Contrastive Multiview Coding

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

Contrastive Multiview Coding (CMC) is a self-supervised learning approach, based on CPC, that learns representations that capture information shared between multiple sensory views. The core idea is to set an anchor view and the sample positive and negative data points from the other view and maximise agreement between positive pairs in learning from two views. Contrastive learning is used to build the embedding.

PaperSourceSee Code · HobbitLong/CMC

Papers archive 2025-07-28

5 shown of 5, 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

13 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
Contrastive Learning4
Self-Supervised Learning2
Activity Recognition1
Cross-Lingual Transfer1
Human Activity Recognition1
Language Modeling1
Language Modelling1
Prediction1
Representation Learning1
Retrieval1
Self-Supervised Action Recognition1
Self-Supervised Image Classification1
Sentence1

Usage over time archive 2025-07-28

Papers per year tagged with Contrastive Multiview Coding: 2019 to 2022, peak 2 2 0 2019: 1 paper 2019 2020: 1 paper 2020 2021: 2 papers 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (5 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 Learning

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