Methods › Computer Vision › Image Data Augmentation › ColorJitter

Color Jitter

ColorJitter

260 papers tagged archive 2025-07-28

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

ColorJitter is a type of image data augmentation where we randomly change the brightness, contrast and saturation of an image.

Image Credit: Apache MXNet

Papers archive 2025-07-28

30 shown of 260, 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 211 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 Learning119
Contrastive Learning116
Representation Learning79
Image Classification36
Data Augmentation34
image-classification23
Transfer Learning18
Object Detection17
General Classification15
object-detection14
Classification11
Linear evaluation11
Semantic Segmentation11
Retrieval9
Object8
Activity Recognition7
Clustering7
Human Activity Recognition7
Image Retrieval7
Segmentation7

Usage over time archive 2025-07-28

Papers per year tagged with ColorJitter: 2013 to 2025, peak 54 54 0 2013: 1 paper 2013 2014: 1 paper 2014 2015: 2 papers 2015 2016: 2 papers 2016 2017: 0 papers 2017 2018: 1 paper 2018 2019: 9 papers 2019 2020: 28 papers 2020 2021: 53 papers 2021 2022: 54 papers 2022 2023: 52 papers 2023 2024: 48 papers 2024 2025: 9 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (260 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

Image Data Augmentation

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