Methods › General › Self-Supervised Learning › Colorization

Colorization

213 papers tagged archive 2025-07-28

Introduced by Richard Zhang et al. in Colorful Image Colorization

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

Colorization is a self-supervision approach that relies on colorization as the pretext task in order to learn image representations.

PaperSource

Papers archive 2025-07-28

30 shown of 213, 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 146 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
Colorization213
Image Colorization85
Generative Adversarial Network13
Decoder11
Super-Resolution11
Line Art Colorization10
Semantic Segmentation10
Semantic correspondence9
Translation9
Denoising8
Diversity8
Image Generation8
SSIM8
Object7
Object Detection7
object-detection7
Representation Learning6
Segmentation6
Sketch Colorization6
Image Restoration5

Usage over time archive 2025-07-28

Papers per year tagged with Colorization: 2016 to 2025, peak 36 36 0 2016: 9 papers 2016 2017: 9 papers 2017 2018: 16 papers 2018 2019: 17 papers 2019 2020: 19 papers 2020 2021: 31 papers 2021 2022: 36 papers 2022 2023: 31 papers 2023 2024: 27 papers 2024 2025: 18 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (213 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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