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Momentum Contrast

MoCo

148 papers tagged archive 2025-07-28

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

MoCo, or Momentum Contrast, is a self-supervised learning algorithm with a contrastive loss.

Contrastive loss methods can be thought of as building dynamic dictionaries. The "keys" (tokens) in the dictionary are sampled from data (e.g., images or patches) and are represented by an encoder network. Unsupervised learning trains encoders to perform dictionary look-up: an encoded “query” should be similar to its matching key and dissimilar to others. Learning is formulated as minimizing a contrastive loss.

MoCo can be viewed as a way to build large and consistent dictionaries for unsupervised learning with a contrastive loss. In MoCo, we maintain the dictionary as a queue of data samples: the encoded representations of the current mini-batch are enqueued, and the oldest are dequeued. The queue decouples the dictionary size from the mini-batch size, allowing it to be large. Moreover, as the dictionary keys come from the preceding several mini-batches, a slowly progressing key encoder, implemented as a momentum-based moving average of the query encoder, is proposed to maintain consistency.

Source: Momentum Contrast for Unsupervised Visual Representation LearningSee Code · facebookresearch/moco

Papers archive 2025-07-28

30 shown of 148, 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 165 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 Learning62
Self-Supervised Learning61
Representation Learning49
Image Classification20
Data Augmentation16
Object Detection14
Linear evaluation13
Semantic Segmentation13
object-detection13
Transfer Learning12
image-classification11
Self-Supervised Image Classification8
Combinatorial Optimization7
Action Recognition6
Classification6
General Classification5
Retrieval5
Image Segmentation4
Instance Segmentation4
Language Modeling4

Usage over time archive 2025-07-28

Papers per year tagged with MoCo: 2019 to 2025, peak 45 45 0 2019: 1 paper 2019 2020: 24 papers 2020 2021: 45 papers 2021 2022: 32 papers 2022 2023: 21 papers 2023 2024: 19 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (148 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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