Methods › Computer Vision › Image Data Augmentation › Random Horizontal Flip

Random Horizontal Flip

78 papers tagged archive 2025-07-28

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

RandomHorizontalFlip is a type of image data augmentation which horizontally flips a given image with a given probability.

Image Credit: Apache MXNet

Papers archive 2025-07-28

30 shown of 78, 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 83 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
Image Classification53
Object Detection45
object-detection29
General Classification24
image-classification22
Object20
Semantic Segmentation15
Instance Segmentation9
Neural Architecture Search7
Classification6
Data Augmentation6
Object Recognition6
Real-Time Object Detection6
Domain Generalization5
Transfer Learning5
Contrastive Learning4
Segmentation4
Self-Supervised Image Classification4
Dichotomous Image Segmentation3
Fine-Grained Image Classification3

Usage over time archive 2025-07-28

Papers per year tagged with Random Horizontal Flip: 2012 to 2022, peak 34 34 0 2012: 1 paper 2012 2013: 2 papers 2013 2014: 3 papers 2014 2015: 3 papers 2015 2016: 6 papers 2016 2017: 7 papers 2017 2018: 12 papers 2018 2019: 34 papers 2019 2020: 9 papers 2020 2021: 0 papers 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (78 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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