Methods › General › Self-Supervised Learning › Jigsaw

Jigsaw

140 papers tagged archive 2025-07-28

Introduced by Mehdi Noroozi et al. in Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

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

Jigsaw is a self-supervision approach that relies on jigsaw-like puzzles as the pretext task in order to learn image representations.

PaperSource

Papers archive 2025-07-28

30 shown of 140, 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 160 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 Learning30
Representation Learning16
Classification11
Image Classification11
Semantic Segmentation9
image-classification9
General Classification8
Anomaly Detection7
Transfer Learning7
Contrastive Learning6
Domain Adaptation6
Domain Generalization6
Object Detection6
Segmentation6
object-detection6
Language Modelling4
Object Recognition4
Position4
Action Recognition3
BIG-bench Machine Learning3

Usage over time archive 2025-07-28

Papers per year tagged with Jigsaw: 2016 to 2025, peak 26 26 0 2016: 2 papers 2016 2017: 7 papers 2017 2018: 7 papers 2018 2019: 8 papers 2019 2020: 21 papers 2020 2021: 26 papers 2021 2022: 22 papers 2022 2023: 26 papers 2023 2024: 14 papers 2024 2025: 7 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (140 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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