Methods › General › Semi-Supervised Learning Methods › MoCo v2

MoCo v2

30 papers tagged archive 2025-07-28

Introduced by Xinlei Chen et al. in Improved Baselines with Momentum Contrastive Learning

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

MoCo v2 is an improved version of the Momentum Contrast self-supervised learning algorithm. Motivated by the findings presented in the SimCLR paper, authors:

These modifications enable MoCo to outperform the state-of-the-art SimCLR with a smaller batch size and fewer epochs.

PaperSourceSee Code · facebookresearch/moco

Papers archive 2025-07-28

30 shown of 30, 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 49 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
Self-Supervised Learning16
Contrastive Learning13
Representation Learning9
Image Classification8
Linear evaluation6
Semantic Segmentation6
image-classification6
Object Detection5
object-detection5
Data Augmentation4
Transfer Learning4
Classification3
Clustering2
General Classification2
Instance Segmentation2
Language Modeling2
Language Modelling2
Object2
Self-Supervised Image Classification2
Semi-Supervised Image Classification2

Usage over time archive 2025-07-28

Papers per year tagged with MoCo v2: 2020 to 2024, peak 9 9 0 2020: 9 papers 2020 2021: 9 papers 2021 2022: 6 papers 2022 2023: 3 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (30 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

Semi-Supervised Learning MethodsSelf-Supervised Learning

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